From bfc3b6e81a5de10b501f906a3592d132e7f8d107 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 10 Dec 2025 09:25:08 +0000 Subject: [PATCH 01/16] add DoubleMLPLPR model to documentation --- doc/api/datasets.rst | 1 + doc/api/dml_models.rst | 1 + 2 files changed, 2 insertions(+) diff --git a/doc/api/datasets.rst b/doc/api/datasets.rst index a1ead85a..29b58c96 100644 --- a/doc/api/datasets.rst +++ b/doc/api/datasets.rst @@ -32,6 +32,7 @@ Dataset Generators plm.datasets.make_plr_CCDDHNR2018 plm.datasets.make_plr_turrell2018 plm.datasets.make_lplr_LZZ2020 + plm.datasets.make_plpr_CP2025 plm.datasets.make_pliv_CHS2015 plm.datasets.make_pliv_multiway_cluster_CKMS2021 plm.datasets.make_confounded_plr_data diff --git a/doc/api/dml_models.rst b/doc/api/dml_models.rst index 77e1104a..450eded7 100644 --- a/doc/api/dml_models.rst +++ b/doc/api/dml_models.rst @@ -17,6 +17,7 @@ doubleml.plm DoubleMLPLR DoubleMLLPLR + DoubleMLPLPR DoubleMLPLIV From 211a7ad0cc148a42eeae2f202fb6f78f522cef72 Mon Sep 17 00:00:00 2001 From: Lukas Kaspras <46094685+LukasKaspras@users.noreply.github.com> Date: Wed, 17 Dec 2025 11:24:26 +0100 Subject: [PATCH 02/16] dix(docu) - revise sensitivity analysis values in workflow.rst Updated the lower bounds for point estimates and statistical uncertainty in the 401(k) example analysis to match the given code output. --- doc/workflow/workflow.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/workflow/workflow.rst b/doc/workflow/workflow.rst index c9acec0e..aa5516f1 100644 --- a/doc/workflow/workflow.rst +++ b/doc/workflow/workflow.rst @@ -431,7 +431,7 @@ relies on conditional exogeneity, which requires to control for confounding vari implements :ref:`sensitivity` with respect to omitted confounders. Analyzing the sensitivity of the intent-to-treat effect in the 401(k) example, we find that the effect remains positive even after adjusting for -omitted confounders with a lower bound of :math:`$4,611` for the point estimate and :math:`$2,359` including statistical uncertainty. +omitted confounders with a lower bound of :math:`$4,353` for the point estimate and :math:`$2,096` including statistical uncertainty. .. tab-set:: From d37c4bff3da6a6b45232e1d76cf0330100e38730 Mon Sep 17 00:00:00 2001 From: Julian Diefenbacher <93731825+JulianDiefenbacher@users.noreply.github.com> Date: Tue, 6 Jan 2026 15:45:05 +0100 Subject: [PATCH 03/16] first version of DoubleMLPLPR docs and example --- doc/conf.py | 4 +- doc/examples/index.rst | 1 + doc/examples/py_double_ml_plpr.ipynb | 3950 ++++++++++++++++++++++++++ doc/guide/models/plm/plm_models.inc | 32 + doc/guide/models/plm/plpr.rst | 12 + doc/literature/literature.rst | 6 + 6 files changed, 4004 insertions(+), 1 deletion(-) create mode 100644 doc/examples/py_double_ml_plpr.ipynb create mode 100644 doc/guide/models/plm/plpr.rst diff --git a/doc/conf.py b/doc/conf.py index b53df08d..bde22ea4 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -275,7 +275,9 @@ # Valid DOI; Causes 403 Client Error: Forbidden for url:... "https://doi.org/10.3982/ECTA15732", # Valid DOI; Causes 403 Client Error: Forbidden for url:... - "https://doi.org/10.1093/ectj/utab019" + "https://doi.org/10.1093/ectj/utab019", + # Valid DOI; Causes 403 Client Error: Forbidden for url:... + "https://doi.org/10.1093/ectj/utaf011" ] # To execute R code via jupyter-execute one needs to install the R kernel for jupyter diff --git a/doc/examples/index.rst b/doc/examples/index.rst index 03f4e713..394134d3 100644 --- a/doc/examples/index.rst +++ b/doc/examples/index.rst @@ -23,6 +23,7 @@ General Examples py_double_ml_apo.ipynb py_double_ml_irm_vs_apo.ipynb py_double_ml_lplr.ipynb + py_double_ml_plpr.ipynb py_double_ml_ssm.ipynb learners/py_optuna.ipynb learners/py_learner.ipynb diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb new file mode 100644 index 00000000..a24a44a8 --- /dev/null +++ b/doc/examples/py_double_ml_plpr.ipynb @@ -0,0 +1,3950 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python: Static Panel Models with Fixed Effects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate treatment effects for static panel models with fixed effects. The estimation is based on [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import optuna\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.base import clone\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "from sklearn.linear_model import LassoCV\n", + "from lightgbm import LGBMRegressor\n", + "\n", + "from doubleml.data import DoubleMLPanelData\n", + "from doubleml.plm.datasets import make_plpr_CP2025\n", + "from doubleml import DoubleMLPLPR\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "We will use the implemented data generating process `make_plpr_CP2025` to generate data similar to the simulation in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011). For exposition, we use the simple linear `dgp_type=\"dgp1\"`, with 150 units, 10 time periods per unit, and a true treatment effect of `theta=0.5`.\n", + "\n", + "Documentation of the data generating process can be also found [API Reference](https://docs.doubleml.org/stable/api/datasets.html).\n", + "\n", + "We set `time_type=\"int\"` such that the time variable values will be integers. It's also possible to use `\"float\"` or `\"datetime\"` time variables with `DoubleMLPLPR`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id time y d x1 x2 x3 x4 \\\n", + "0 1 1 -1.290479 0.908307 1.710715 -1.853675 -1.473907 1.366514 \n", + "1 1 2 -2.850646 -1.316777 -0.325043 4.178599 -1.159857 -0.139527 \n", + "2 1 3 -4.338502 -1.756120 -0.897590 1.505972 -0.925189 1.511500 \n", + "3 1 4 -2.713236 0.934866 1.987849 2.596228 -0.220666 -0.480717 \n", + "4 1 5 -5.782997 -4.357881 -3.086559 3.796975 -1.539641 -2.425617 \n", + "\n", + " x5 x6 ... x21 x22 x23 x24 x25 \\\n", + "0 -0.322024 2.944020 ... -1.828362 -3.010547 -0.840202 -3.085159 1.169952 \n", + "1 -0.230115 -0.631976 ... -0.724172 -0.421045 -2.012480 -2.081784 -2.734123 \n", + "2 -2.206561 0.132579 ... 1.766109 -2.252858 -2.919826 -1.974066 -0.773881 \n", + "3 -3.966273 -0.911226 ... 0.856124 0.727759 -0.501579 1.077504 2.268052 \n", + "4 -1.020599 -1.666200 ... 2.617215 -1.231835 -0.891350 0.246981 2.489642 \n", + "\n", + " x26 x27 x28 x29 x30 \n", + "0 -0.954107 -3.925198 -0.779510 -0.430700 1.004298 \n", + "1 -0.879470 -2.141218 4.598401 -4.222797 -2.523024 \n", + "2 0.244633 -1.727550 1.665467 0.562291 -1.553616 \n", + "3 -3.821422 1.629055 -0.220834 -1.185091 -5.462884 \n", + "4 0.319735 -2.810366 0.585826 3.643749 0.147147 \n", + "\n", + "[5 rows x 34 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "data = make_plpr_CP2025(num_id=150, num_t=10, dim_x=30, theta=0.5, dgp_type=\"dgp1\", time_type=\"int\")\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a corresponding `DoubleMLPanelData` object, we need to set `static_panel=True` and specify `id` and `time` columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_obj = DoubleMLPanelData(data, y_col=\"y\", d_cols=\"d\", t_col=\"time\", id_col=\"id\", static_panel=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The partially linear panel regression (PLPR) model extends the partially linear model to panel data by introducing fixed effects $\\alpha_i$.\n", + "\n", + "Partialled-out PLPR (PO-PLPR) model:\n", + "\n", + "\\begin{align*}\n", + " Y_{it} &= \\theta_0 D_{it} + g_0(X_{it}) + \\alpha_i + U_{it} \\\\\n", + " D_{it} &= m_0(X_{it}) + \\gamma_i + V_{it}\n", + "\\end{align*}\n", + "\n", + "- $Y_{it}$ outcome, $D_{it}$ treatment, $X_{it}$ covariates, $\\theta_0$ causal treatment effect\n", + "- $g_0$ and $m_0$ nuisance functions\n", + "- $\\alpha_i$, $\\gamma_i$ unobserved individual heterogeneity, correlated with covariates\n", + "- $U_{it}$, $V_{it}$ error terms\n", + "\n", + "Further note $\\mathbb{E}[U_{it} \\mid D_{it}, X_{it}, \\alpha_i] = 0$, but $\\mathbb{E}[\\alpha_i \\mid D_{it}, X_{it}] \\neq 0$, and $\\mathbb{E}[V_{it} \\mid X_{it}, \\gamma_i]=0$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assumptions\n", + "\n", + "Define potential outcomes $Y_{it}(d)$ for individual $i$ at wave $t$, where realizations are linked to the observed outcome by the consistency assumption that\n", + "$Y_{it}(d_{it}) = Y_{it}$. \n", + "\n", + "$\\xi_i$ are time-invariant heterogeneity terms influencing outcome and treatment. Further, $L_{t-1}(W_i) = \\{ W_{i1}, \\dots, W_{it-1} \\}$, lags of a random variable $W_{it}$ at wave $t$.\n", + "\n", + "- No feedback to predictors\n", + "$$ X_{it} \\perp L_{t-1} (Y_i, D_i) \\mid L_{t-1} (X_i), \\xi_i $$\n", + "- Static panel\n", + "$$ Y_{it}, D_{it} \\perp L_{t-1} (Y_i, X_i, D_i) \\mid X_{it}, \\xi_i $$\n", + "- Selection on observables and omitted time-invariant variables\n", + "$$ Y_{it} (.) \\perp D_{it} \\mid X_{it}, \\xi_i $$\n", + "- Homogeneity and linearity of the treatment effect\n", + "$$ \\mathbb{E} [Y_{it}(d) - Y_{it}(0) \\mid X_{it}, \\xi_i] = d \\theta_0 $$\n", + "- Additive Separability\n", + "\\begin{align*}\n", + "\\mathbb{E} [Y_{it}(0) \\mid X_{it}, \\xi_i] &= g_1(X_{it}) + \\alpha^*_i \\quad \\text{where } \\alpha^*_i = \\alpha^*(\\xi_i), \\\\\n", + "\\mathbb{E} [D_{it} \\mid X_{it}, \\xi_i] &= m_1(X_{it}) + \\gamma_i \\quad \\text{where } \\gamma_i = \\gamma(\\xi_i)\n", + "\\end{align*} \n", + "\n", + "For more information, see [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To estimate the causal effect, we can create a `DoubleMLPLPR` object. \n", + "\n", + "The model described in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) uses block-k-fold cross-fitting, where the entire time series of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit which is common with panel data. Furthermore, cluster robust standard error are employed. `DoubleMLPLPR` implements both aspects by using `id_col` as the cluster variable.\n", + "\n", + "[Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) describes multiple estimation approaches, which can be set with the `approach` parameter. Depending on the type of `approach`, different data transformation are performed along the way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Approaches\n", + "\n", + "#### Correlated Random Effect\n", + "\n", + "The Correlated random effect approaches `cre_general` and `cre_normal`\n", + "\n", + "`cre_general` approach:\n", + "\n", + "- Learning $g_0$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$\n", + "- First learning $\\tilde{m}_0({\\cdot})$ from $\\{ D_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$ with prediction $\\hat{m}_{0it} = \\tilde{m}_0 (X_{it}, \\bar{X}_i) $\n", + " - Calculate $\\hat{\\bar{m}}_i = T^{-1} \\sum_{t=1}^T \\hat{m}_{0it} $\n", + " - Calculate final nuisance part as $ \\hat{m}^*_0 (X_{it}, \\bar{X}_i, \\bar{D}_i) = \\hat{m}_{0it} + \\bar{D}_i - \\hat{\\bar{m}}_i $ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "learner = LassoCV()\n", + "ml_l = clone(learner)\n", + "ml_m = clone(learner)\n", + "\n", + "dml_plpr_cre_general = DoubleMLPLPR(data_obj, ml_l=ml_l, ml_m=ml_m, approach=\"cre_general\", n_folds=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can look at the the transformed data using the `data_transform` property after the `DoubleMLPLPR` object was created." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id time y d x1 x2 x3 x4 \\\n", + "0 1 1 -1.290479 0.908307 1.710715 -1.853675 -1.473907 1.366514 \n", + "1 1 2 -2.850646 -1.316777 -0.325043 4.178599 -1.159857 -0.139527 \n", + "2 1 3 -4.338502 -1.756120 -0.897590 1.505972 -0.925189 1.511500 \n", + "3 1 4 -2.713236 0.934866 1.987849 2.596228 -0.220666 -0.480717 \n", + "4 1 5 -5.782997 -4.357881 -3.086559 3.796975 -1.539641 -2.425617 \n", + "\n", + " x5 x6 ... x21_mean x22_mean x23_mean x24_mean x25_mean \\\n", + "0 -0.322024 2.944020 ... 1.24018 -0.52821 -0.734145 0.227494 1.164763 \n", + "1 -0.230115 -0.631976 ... 1.24018 -0.52821 -0.734145 0.227494 1.164763 \n", + "2 -2.206561 0.132579 ... 1.24018 -0.52821 -0.734145 0.227494 1.164763 \n", + "3 -3.966273 -0.911226 ... 1.24018 -0.52821 -0.734145 0.227494 1.164763 \n", + "4 -1.020599 -1.666200 ... 1.24018 -0.52821 -0.734145 0.227494 1.164763 \n", + "\n", + " x26_mean x27_mean x28_mean x29_mean x30_mean \n", + "0 0.412979 -1.272608 0.459816 -0.829863 -1.145189 \n", + "1 0.412979 -1.272608 0.459816 -0.829863 -1.145189 \n", + "2 0.412979 -1.272608 0.459816 -0.829863 -1.145189 \n", + "3 0.412979 -1.272608 0.459816 -0.829863 -1.145189 \n", + "4 0.412979 -1.272608 0.459816 -0.829863 -1.145189 \n", + "\n", + "[5 rows x 64 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_plpr_cre_general.data_transform.data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the covariates inlcude the original $X_{it}$ and additionally the unit mean $\\bar{X}_i$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After fitting the model, we can print the `DoubleMLPLPR` object.\n", + "\n", + "The Data Summary corresponds to the transformed data. Additional Information at the end also includes a Pre-Transformation Data Summary." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLPLPR Object ==================\n", + "\n", + "------------------ Data Summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x1_mean', 'x2_mean', 'x3_mean', 'x4_mean', 'x5_mean', 'x6_mean', 'x7_mean', 'x8_mean', 'x9_mean', 'x10_mean', 'x11_mean', 'x12_mean', 'x13_mean', 'x14_mean', 'x15_mean', 'x16_mean', 'x17_mean', 'x18_mean', 'x19_mean', 'x20_mean', 'x21_mean', 'x22_mean', 'x23_mean', 'x24_mean', 'x25_mean', 'x26_mean', 'x27_mean', 'x28_mean', 'x29_mean', 'x30_mean']\n", + "Instrument variable(s): None\n", + "Time variable: time\n", + "Id variable: id\n", + "Static panel data: True\n", + "No. Unique Ids: 150\n", + "No. Observations: 1500\n", + "\n", + "------------------ Score & Algorithm ------------------\n", + "Score function: partialling out\n", + "Static panel model approach: cre_general\n", + "\n", + "------------------ Machine Learner ------------------\n", + "Learner ml_l: LassoCV()\n", + "Learner ml_m: LassoCV()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_l RMSE: [[1.8336022]]\n", + "Learner ml_m RMSE: [[0.99534683]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds per cluster: 5\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit Summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.490173 0.026161 18.737077 2.468215e-78 0.438899 0.541447\n", + "\n", + "------------------ Additional Information -------------\n", + "Cluster variable(s): ['id']\n", + "\n", + "Pre-Transformation Data Summary: \n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30']\n", + "No. Observations: 1500\n", + "\n" + ] + } + ], + "source": [ + "dml_plpr_cre_general.fit()\n", + "print(dml_plpr_cre_general)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`cre_normal` approach:\n", + "\n", + "Under the assumption that the conditional distribution $ D_{i1}, \\dots, D_{iT} \\mid X_{i1}, \\dots X_{iT} $ is multivariate normal\n", + "- Learn $g_0$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$\n", + "- Learn $m^*_{0}$ from $\\{ D_{it}, X_{it}, \\bar{X}_i, \\bar{D}_i: t=1,\\dots, T \\}_{i=1}^N$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.505807 0.027168 18.617927 2.299468e-77 0.45256 0.559055\n" + ] + } + ], + "source": [ + "dml_plpr_cre_normal = DoubleMLPLPR(data_obj, ml_l=ml_l, ml_m=ml_m, approach=\"cre_normal\", n_folds=5)\n", + "dml_plpr_cre_normal.fit()\n", + "print(dml_plpr_cre_normal.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `cre_normal` approach uses additionally inlcudes $\\bar{D}_i$ in the treatment nuisance estimation. The corresponding data can be assesses by the `d_mean` property." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94278854],\n", + " [-0.94278854],\n", + " [-0.94278854],\n", + " ...,\n", + " [ 0.15320478],\n", + " [ 0.15320478],\n", + " [ 0.15320478]], shape=(1500, 1))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_plpr_cre_normal.d_mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Transformation Approaches\n", + "\n", + "`fd_exact` approach:\n", + "\n", + "Consider FD transformation $Q(Y_{it})= Y_{it} - Y_{it-1} $, under Assumptions from above, transformed nuisance function can be learnt as\n", + "\n", + "- $ \\Delta g_0 (X_{it-1}, X_{it}) $ from $ \\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $\n", + "- $ \\Delta m_0 (X_{it-1}, X_{it}) $ from $ \\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 34 columns

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" + ], + "text/plain": [ + " id time y_demean d_demean x1_demean x2_demean x3_demean x4_demean \\\n", + "0 1 1 1.543571 1.760660 2.207607 -2.039516 -0.642847 1.014204 \n", + "1 1 2 -0.016596 -0.464424 0.171849 3.992759 -0.328797 -0.491837 \n", + "2 1 3 -1.504452 -0.903767 -0.400698 1.320131 -0.094129 1.159190 \n", + "3 1 4 0.120814 1.787219 2.484741 2.410387 0.610394 -0.833027 \n", + "4 1 5 -2.948947 -3.505528 -2.589667 3.611134 -0.708582 -2.777927 \n", + "\n", + " x5_demean x6_demean ... x21_demean x22_demean x23_demean x24_demean \\\n", + "0 0.384166 1.826013 ... -3.162933 -2.475942 0.000728 -3.344219 \n", + "1 0.476074 -1.749982 ... -2.058743 0.113560 -1.171550 -2.340844 \n", + "2 -1.500371 -0.985427 ... 0.431538 -1.718253 -2.078895 -2.233126 \n", + "3 -3.260084 -2.029232 ... -0.478447 1.262364 0.339352 0.818443 \n", + "4 -0.314410 -2.784206 ... 1.282645 -0.697230 -0.050420 -0.012080 \n", + "\n", + " x25_demean x26_demean x27_demean x28_demean x29_demean x30_demean \n", + "0 0.082829 -1.351303 -2.670511 -1.275344 0.407596 2.187878 \n", + "1 -3.821246 -1.276666 -0.886531 4.102567 -3.384501 -1.339444 \n", + "2 -1.861004 -0.152563 -0.472863 1.169632 1.400587 -0.370036 \n", + "3 1.180930 -4.218618 2.883741 -0.716668 -0.346795 -4.279304 \n", + "4 1.402519 -0.077461 -1.555679 0.089991 4.482045 1.330727 \n", + "\n", + "[5 rows x 34 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_plpr_wg_approx = DoubleMLPLPR(data_obj, ml_l=ml_l, ml_m=ml_m, approach=\"wg_approx\", n_folds=5)\n", + "dml_plpr_wg_approx.data_transform.data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the outcome, treatment and covariate variables are now labeled `y_deman`, `d_deman`, `xi_deman` to indicate the within-group transformations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_demean 0.495323 0.025841 19.167824 6.872435e-82 0.444675 0.545972\n" + ] + } + ], + "source": [ + "dml_plpr_wg_approx.fit()\n", + "print(dml_plpr_wg_approx.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the simple linear data generating process `dgp_type=\"dgp1\"`, we can see that all approaches lead to estimated close the true effect of `theta=0.5`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `data_original` property additionally includes the original data before any transformation was applied." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id time y d x1 x2 x3 x4 \\\n", + "0 1 1 -1.290479 0.908307 1.710715 -1.853675 -1.473907 1.366514 \n", + "1 1 2 -2.850646 -1.316777 -0.325043 4.178599 -1.159857 -0.139527 \n", + "2 1 3 -4.338502 -1.756120 -0.897590 1.505972 -0.925189 1.511500 \n", + "3 1 4 -2.713236 0.934866 1.987849 2.596228 -0.220666 -0.480717 \n", + "4 1 5 -5.782997 -4.357881 -3.086559 3.796975 -1.539641 -2.425617 \n", + "\n", + " x5 x6 ... x21 x22 x23 x24 x25 \\\n", + "0 -0.322024 2.944020 ... -1.828362 -3.010547 -0.840202 -3.085159 1.169952 \n", + "1 -0.230115 -0.631976 ... -0.724172 -0.421045 -2.012480 -2.081784 -2.734123 \n", + "2 -2.206561 0.132579 ... 1.766109 -2.252858 -2.919826 -1.974066 -0.773881 \n", + "3 -3.966273 -0.911226 ... 0.856124 0.727759 -0.501579 1.077504 2.268052 \n", + "4 -1.020599 -1.666200 ... 2.617215 -1.231835 -0.891350 0.246981 2.489642 \n", + "\n", + " x26 x27 x28 x29 x30 \n", + "0 -0.954107 -3.925198 -0.779510 -0.430700 1.004298 \n", + "1 -0.879470 -2.141218 4.598401 -4.222797 -2.523024 \n", + "2 0.244633 -1.727550 1.665467 0.562291 -1.553616 \n", + "3 -3.821422 1.629055 -0.220834 -1.185091 -5.462884 \n", + "4 0.319735 -2.810366 0.585826 3.643749 0.147147 \n", + "\n", + "[5 rows x 34 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dml_plpr_wg_approx.data_original.data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature preprocessing pipelines\n", + "\n", + "We can incorporate preprocessing pipelines. For example, when using Lasso, we may want to include polynomial and interaction terms. Here, we create a class that allows us to include, for example, polynomials of order 3 and interactions between all variables." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class PolyPlus(BaseEstimator, TransformerMixin):\n", + " \"\"\"PolynomialFeatures(degree=k) and additional terms x_i^(k+1).\"\"\"\n", + "\n", + " def __init__(self, degree=2, interaction_only=False, include_bias=False):\n", + " self.degree = degree\n", + " self.extra_degree = degree + 1\n", + " self.interaction_only = interaction_only\n", + " self.include_bias = include_bias\n", + " self.poly = PolynomialFeatures(degree=degree, interaction_only=interaction_only, include_bias=include_bias)\n", + "\n", + " def fit(self, X, y=None):\n", + " self.poly.fit(X)\n", + " self.n_features_in_ = X.shape[1]\n", + " return self\n", + "\n", + " def transform(self, X):\n", + " X = np.asarray(X)\n", + " X_poly = self.poly.transform(X)\n", + " X_extra = X ** self.extra_degree\n", + " return np.hstack([X_poly, X_extra])\n", + "\n", + " def get_feature_names_out(self, input_features=None):\n", + " input_features = np.array(\n", + " input_features\n", + " if input_features is not None\n", + " else [f\"x{i}\" for i in range(self.n_features_in_)]\n", + " )\n", + " poly_names = self.poly.get_feature_names_out(input_features)\n", + " extra_names = [f\"{name}^{self.extra_degree}\" for name in input_features]\n", + " return np.concatenate([poly_names, extra_names])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example we use the non-linear and discontinuous `dgp_type=\"dgp3\"`, with 30 covariates and a true treatment effect `theta=0.5`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dim_x = 30\n", + "theta = 0.5\n", + "\n", + "np.random.seed(123)\n", + "data_dgp3 = make_plpr_CP2025(num_id=500, num_t=10, dim_x=dim_x, theta=theta, dgp_type=\"dgp3\")\n", + "dml_data_dgp3 = DoubleMLPanelData(data_dgp3, y_col=\"y\", d_cols=\"d\", t_col=\"time\", id_col=\"id\", static_panel=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply the polynomial and intercation transformation for specific sets of covariates. For example, for the `fd_exact` approach, we can apply it to the original $X_{it}$ and lags $X_{it-1}$ seperately using `ColumnTransformer`.\n", + "\n", + "To achieve this, we pass need to pass the corresponding indices for these two sets. `DoubleMLPLPR` stacks sets $X_{it}$ and $X_{it-1}$ column-wise. Given our example data has 30 covariates, this means that the first 30 features in the nuisance estimation correspond to the original $X_{it}$, and the last 30 correspond to lags $X_{it-1}$. Therefore we define the indices `indices_x` and `indices_x_tr` as below." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "indices_x = [x for x in range(dim_x)]\n", + "indices_x_tr = [x + dim_x for x in indices_x]\n", + "\n", + "preprocessor = ColumnTransformer(\n", + " [\n", + " (\n", + " \"poly_x\",\n", + " PolyPlus(degree=2, include_bias=False, interaction_only=False),\n", + " indices_x,\n", + " ),\n", + " (\n", + " \"poly_x_tr\",\n", + " PolyPlus(degree=2, include_bias=False, interaction_only=False),\n", + " indices_x_tr,\n", + " ),\n", + " ],\n", + " remainder=\"passthrough\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This preprocessor can be applied for approaches `cre_general` and `cre_normal` in the same fashion. In this case the two sets of covariates would be the original $X_{it}$ and the unit mean $\\bar{X}_i$.\n", + "\n", + "**Remark**: Note that we set `remainder=\"passthrough\"` such that all remaining features, not part of `indices_x` and `indices_x_tr`, would not be preprocessed but still included in the nuisance estimation. This is particularly important for the `cre_normal` approach, as $\\bar{D}_i$ is further added to $X_{it}$ and $\\bar{X}_i$ in the treatment nuisance model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can create the learner using a pipeline and fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('poly_x', PolyPlus(),\n",
+       "                                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+       "                                                   10, 11, 12, 13, 14, 15, 16,\n",
+       "                                                   17, 18, 19, 20, 21, 22, 23,\n",
+       "                                                   24, 25, 26, 27, 28, 29]),\n",
+       "                                                 ('poly_x_tr', PolyPlus(),\n",
+       "                                                  [30, 31, 32, 33, 34, 35, 36,\n",
+       "                                                   37, 38, 39, 40, 41, 42, 43,\n",
+       "                                                   44, 45, 46, 47, 48, 49, 50,\n",
+       "                                                   51, 52, 53, 54, 55, 56, 57,\n",
+       "                                                   58, 59])])),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('lassocv', LassoCV(alphas=20, cv=2, n_jobs=5))])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('columntransformer',\n", + " ColumnTransformer(remainder='passthrough',\n", + " transformers=[('poly_x', PolyPlus(),\n", + " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", + " 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23,\n", + " 24, 25, 26, 27, 28, 29]),\n", + " ('poly_x_tr', PolyPlus(),\n", + " [30, 31, 32, 33, 34, 35, 36,\n", + " 37, 38, 39, 40, 41, 42, 43,\n", + " 44, 45, 46, 47, 48, 49, 50,\n", + " 51, 52, 53, 54, 55, 56, 57,\n", + " 58, 59])])),\n", + " ('standardscaler', StandardScaler()),\n", + " ('lassocv', LassoCV(alphas=20, cv=2, n_jobs=5))])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ml_lasso = make_pipeline(\n", + " preprocessor, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + ")\n", + "\n", + "ml_lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_diff 0.516685 0.018054 28.619386 3.855865e-180 0.481301 0.55207\n" + ] + } + ], + "source": [ + "plpr_lasso_fd = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso), clone(ml_lasso), approach=\"fd_exact\", n_folds=5)\n", + "plpr_lasso_fd.fit(store_models=True)\n", + "print(plpr_lasso_fd.summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given that we apply the polynomial and interactions preprossing to two sets of 30 columns each, the number of features is 1050." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1050" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plpr_lasso_fd.models[\"ml_m\"][\"d_diff\"][0][0].named_steps[\"lassocv\"].n_features_in_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As describes above, for the `cre_normal` approach adds $\\bar{X}_i$ to the features used in the treatment nuisance estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.552196 0.028428 19.424151 4.822927e-84 0.496478 0.607915\n" + ] + }, + { + "data": { + "text/plain": [ + "1051" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plpr_lasso_cre_normal = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso), clone(ml_lasso), approach=\"cre_normal\", n_folds=5)\n", + "plpr_lasso_cre_normal.fit(store_models=True)\n", + "print(plpr_lasso_cre_normal.summary)\n", + "plpr_lasso_cre_normal.models[\"ml_m\"][\"d\"][0][0].named_steps[\"lassocv\"].n_features_in_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the `wg_approx` approach, there is only one set of features. We can create a similar learner for this setting." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "preprocessor_wg = ColumnTransformer(\n", + " [\n", + " (\n", + " \"poly_x\",\n", + " PolyPlus(degree=2, include_bias=False, interaction_only=False),\n", + " indices_x,\n", + " )\n", + " ],\n", + " remainder=\"passthrough\",\n", + ")\n", + "\n", + "ml_lasso_wg = make_pipeline(\n", + " preprocessor_wg, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_demean 1.150157 0.014083 81.671376 0.0 1.122555 1.177758\n" + ] + }, + { + "data": { + "text/plain": [ + "525" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plpr_lasso_wg = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso_wg), clone(ml_lasso_wg), approach=\"wg_approx\", n_folds=5)\n", + "plpr_lasso_wg.fit(store_models=True)\n", + "print(plpr_lasso_wg.summary)\n", + "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps[\"lassocv\"].n_features_in_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that for the more complicated data generating process `dgp3`, the approximation approach performs worse compared to the other approaches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As another example, below we should how to select a specific covariate subset for preprocessing. This can be useful in case of the data includes dummy covariates, where adding polynomials might not be appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "39" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_cols = dml_data_dgp3.x_cols \n", + "x_cols_to_pre = [\"x3\", \"x6\", \"x22\"]\n", + "\n", + "indices_x_pre = [i for i, c in enumerate(x_cols) if c in x_cols_to_pre]\n", + "\n", + "preprocessor_alt = ColumnTransformer(\n", + " [\n", + " (\n", + " \"poly_x\",\n", + " PolyPlus(degree=2, include_bias=False, interaction_only=False),\n", + " indices_x_pre,\n", + " )\n", + " ],\n", + " remainder=\"passthrough\",\n", + ")\n", + "ml_lasso_alt = make_pipeline(\n", + " preprocessor_alt, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + ")\n", + "\n", + "plpr_lasso_wg.learner[\"ml_l\"] = ml_lasso_alt\n", + "plpr_lasso_wg.learner[\"ml_m\"] = ml_lasso_alt\n", + "\n", + "plpr_lasso_wg.fit(store_models=True)\n", + "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps[\"lassocv\"].n_features_in_" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ColumnTransformer(remainder='passthrough',\n",
+       "                  transformers=[('poly_x', PolyPlus(), [2, 5, 21])])
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" + ], + "text/plain": [ + "ColumnTransformer(remainder='passthrough',\n", + " transformers=[('poly_x', PolyPlus(), [2, 5, 21])])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps['columntransformer']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at the resulting features.\n", + "\n", + "**Remark**: Note, however, that the feature names here refer only to the corresponding `x_cols` indices, not the column names from the `pd.DataFrame` because `DoubleML` uses `np.array`'s for fitting the model. Therefore the difference to the names from `x_cols_to_pre`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['poly_x__x2', 'poly_x__x5', 'poly_x__x21', 'poly_x__x2^2',\n", + " 'poly_x__x2 x5', 'poly_x__x2 x21', 'poly_x__x5^2',\n", + " 'poly_x__x5 x21', 'poly_x__x21^2', 'poly_x__x2^3', 'poly_x__x5^3',\n", + " 'poly_x__x21^3', 'remainder__x0', 'remainder__x1', 'remainder__x3',\n", + " 'remainder__x4', 'remainder__x6', 'remainder__x7', 'remainder__x8',\n", + " 'remainder__x9', 'remainder__x10', 'remainder__x11',\n", + " 'remainder__x12', 'remainder__x13', 'remainder__x14',\n", + " 'remainder__x15', 'remainder__x16', 'remainder__x17',\n", + " 'remainder__x18', 'remainder__x19', 'remainder__x20',\n", + " 'remainder__x22', 'remainder__x23', 'remainder__x24',\n", + " 'remainder__x25', 'remainder__x26', 'remainder__x27',\n", + " 'remainder__x28', 'remainder__x29'], dtype=object)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps['columntransformer'].get_feature_names_out()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hyperparameter tuning\n", + "\n", + "In this section we will use the `tune_ml_models()` method to tune hyperparameters using the [Optuna](https://optuna.org/) package. More details can found in the [Python: Hyperparametertuning with Optuna](https://docs.doubleml.org/stable/examples/learners/py_optuna.html) example notebook.\n", + "\n", + "As an example, we use [LightGBM](https://lightgbm.readthedocs.io/en/stable/) regressors and compare the estimates for the different static panel model approaches, when applied to the non-linear and discontinuous `dgp3`." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(11)\n", + "data_tune = make_plpr_CP2025(num_id=4000, num_t=10, dim_x=dim_x, theta=theta, dgp_type=\"dgp3\")\n", + "dml_data_tune = DoubleMLPanelData(data_tune, y_col=\"y\", d_cols=\"d\", t_col=\"time\", id_col=\"id\", static_panel=True)\n", + "ml_boost = LGBMRegressor(random_state=314, verbose=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# parameter space for both ml models\n", + "def ml_params(trial):\n", + " return {\n", + " \"n_estimators\": 100,\n", + " \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.1, 0.4, log=True),\n", + " \"max_depth\": trial.suggest_int(\"max_depth\", 2, 10),\n", + " \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 1, 5),\n", + " \"reg_lambda\": trial.suggest_float(\"reg_lambda\", 1e-2, 5, log=True),\n", + " }\n", + "\n", + "param_space = {\n", + " \"ml_l\": ml_params,\n", + " \"ml_m\": ml_params\n", + "}\n", + "\n", + "optuna_settings = {\n", + " \"n_trials\": 100,\n", + " \"show_progress_bar\": True,\n", + " \"verbosity\": optuna.logging.WARNING, # Suppress Optuna logs\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Best trial: 41. Best value: -1.45877: 100%|██████████| 100/100 [02:29<00:00, 1.49s/it]\n", + "Best trial: 97. Best value: -1.19917: 100%|██████████| 100/100 [02:30<00:00, 1.51s/it]\n" + ] + }, + { + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = sns.color_palette(\"colorblind\")\n", + "\n", + "ci_cre_general = plpr_tune_cre_general.confint()\n", + "ci_cre_normal = plpr_tune_cre_normal.confint()\n", + "ci_fd = plpr_tune_fd.confint()\n", + "ci_wg = plpr_tune_wg.confint()\n", + "\n", + "comparison_data = {\n", + " \"Model\": [\"cre_general\", \"cre_normal\", \"fd\", \"wg\"],\n", + " \"theta\": [plpr_tune_cre_general.coef[0], plpr_tune_cre_normal.coef[0], plpr_tune_fd.coef[0], plpr_tune_wg.coef[0]],\n", + " \"se\": [plpr_tune_cre_general.se[0], plpr_tune_cre_normal.se[0], plpr_tune_fd.se[0], plpr_tune_wg.se[0]],\n", + " \"ci_lower\": [ci_cre_general.iloc[0, 0], ci_cre_normal.iloc[0, 0], ci_fd.iloc[0, 0], ci_wg.iloc[0, 0]],\n", + " \"ci_upper\": [ci_cre_general.iloc[0, 1], ci_cre_normal.iloc[0, 1], ci_fd.iloc[0, 1], ci_wg.iloc[0, 1]]\n", + "}\n", + "df_comparison = pd.DataFrame(comparison_data)\n", + "\n", + "print(f\"True treatment effect: {theta}\\n\")\n", + "print(df_comparison.to_string(index=False))\n", + "\n", + "# Create comparison plot \n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "for i in range(len(df_comparison)):\n", + " plt.errorbar(i, df_comparison.loc[i, \"theta\"],\n", + " yerr=[[df_comparison.loc[i, \"theta\"] - df_comparison.loc[i, \"ci_lower\"]],\n", + " [df_comparison.loc[i, \"ci_upper\"] - df_comparison.loc[i, \"theta\"]]],\n", + " fmt='o', capsize=5, capthick=2, ecolor=palette[i], color=palette[i],\n", + " label=df_comparison.loc[i, \"Model\"], markersize=10, zorder=2)\n", + "plt.axhline(y=theta, color=palette[4], linestyle='--',\n", + " linewidth=2, label=\"True effect\", zorder=1)\n", + "\n", + "plt.title(\"Comparison across ML models for First-Difference approach\")\n", + "plt.ylabel(\"Coefficient Value\")\n", + "plt.xticks(range(4), df_comparison[\"Model\"], rotation=15, ha=\"right\")\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We again see that the `wg_approx` leads to a biased estimate in the non-linear and discontinuous `dgp3` setting. The approaches `cre_general`, `cre_normal`, `fd_exact`, in combination with [LightGBM](https://lightgbm.readthedocs.io/en/stable/) regressors, tuned using the [Optuna](https://optuna.org/) package, lead to estimate close to the true treatment effect.\n", + "\n", + "This is line with the simulation results in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011), albeit only for only one dataset in this example." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/guide/models/plm/plm_models.inc b/doc/guide/models/plm/plm_models.inc index 32fceeec..824ab840 100644 --- a/doc/guide/models/plm/plm_models.inc +++ b/doc/guide/models/plm/plm_models.inc @@ -99,6 +99,38 @@ Logistic partially linear regression model (LPLR) dml_lplr_obj.fit().summary +.. _plpr-model: + +Partially linear panel regression model (PLPR) +********************************************** + +.. include:: /guide/models/plm/plpr.rst + +``DoubleMLPLPR`` implements PLPR models. Estimation is conducted via its ``fit()`` method. + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.plm.datasets import make_plpr_CP2025 + from sklearn.linear_model import LassoCV + from sklearn.base import clone + np.random.seed(3142) + learner = LassoCV() + ml_l = clone(learner) + ml_m = clone(learner) + data = make_plpr_CP2025(num_id=250, num_t=10, dim_x=30, theta=0.5, dgp_type='dgp1') + obj_dml_data = DoubleMLPanelData(data, 'y', 'd', 'time', 'id', static_panel=True) + dml_plpr_obj = DoubleMLPLPR(obj_dml_data, ml_l, ml_m) + dml_plpr_obj.fit() + print(dml_plpr_obj) + + .. _pliv-model: Partially linear IV regression model (PLIV) diff --git a/doc/guide/models/plm/plpr.rst b/doc/guide/models/plm/plpr.rst new file mode 100644 index 00000000..67e0260a --- /dev/null +++ b/doc/guide/models/plm/plpr.rst @@ -0,0 +1,12 @@ +**Partially linear panel regression (PLPR)** models by `Clarke and Polselli (2025) ` take the form + +.. math:: + + Y_{it} = \theta_0 D_{it} + g_0(X_{it}) + \alpha_i + U_{it}, & &\mathbb{E}(U_{it} | D_{it}, X_{it}, \alpha_i) = 0, + + D_{it} = m_0(X_{it}) + \gamma_i + V_{it}, & &\mathbb{E}(V_{it} | X_{it}, \gamma_i) = 0, + +where :math:`Y_{it}` is the outcome variable and :math:`D_{it}` is the policy variable of interest. +The high-dimensional vector :math:`X_{it} = (X_{it,1}, \ldots, X_{it,p})` consists of other confounding covariates. +:math:`\alpha_i` and :math:`\gamma_i` represent unobserved individual heterogeneity terms, correlated with the covariates +and :math:`U_{it}` and :math:`V_{it}` are stochastic errors. \ No newline at end of file diff --git a/doc/literature/literature.rst b/doc/literature/literature.rst index 4869223e..ecde52e7 100644 --- a/doc/literature/literature.rst +++ b/doc/literature/literature.rst @@ -142,6 +142,12 @@ Double Machine Learning Literature :octicon:`link` :bdg-link-dark:`URL ` |hr| + - Paul S Clarke, Annalivia Polselli |br| + **Double machine learning for static panel models with fixed effects** |br| + *The Econometrics Journal, utaf011, 2025* |br| + :octicon:`link` :bdg-link-dark:`URL ` + |hr| + - Yusuke Narita, Shota Yasui, Kohei Yata |br| **Debiased Off-Policy Evaluation for Recommendation Systems** |br| *RecSys '21: Fifteenth ACM Conference on Recommender Systems, 372–379, 2021* |br| From 12b20c0ba98004735c5ee18ba4ddd764b7758744 Mon Sep 17 00:00:00 2001 From: JulianDiefenbacher Date: Sun, 11 Jan 2026 00:14:52 +0100 Subject: [PATCH 04/16] update plpr example, model, score docs, funding --- doc/examples/py_double_ml_plpr.ipynb | 207 +++++++++++++++------------ doc/guide/data/panel_data.rst | 29 +++- doc/guide/models/plm/plpr.rst | 183 ++++++++++++++++++++++- doc/guide/scores/plm/plm_scores.inc | 7 + doc/guide/scores/plm/plpr_score.rst | 111 ++++++++++++++ doc/index.rst | 2 +- 6 files changed, 437 insertions(+), 102 deletions(-) create mode 100644 doc/guide/scores/plm/plpr_score.rst diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb index a24a44a8..9db17da7 100644 --- a/doc/examples/py_double_ml_plpr.ipynb +++ b/doc/examples/py_double_ml_plpr.ipynb @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate treatment effects for static panel models with fixed effects. The estimation is based on [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." + "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate treatment effects for static panel models with fixed effects in a partially linear panel regression [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) model. The model is based on [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." ] }, { @@ -50,11 +50,9 @@ "source": [ "## Data\n", "\n", - "We will use the implemented data generating process `make_plpr_CP2025` to generate data similar to the simulation in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011). For exposition, we use the simple linear `dgp_type=\"dgp1\"`, with 150 units, 10 time periods per unit, and a true treatment effect of `theta=0.5`.\n", + "We will use the implemented data generating process [make_plpr_CP2025](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plpr_CP2025.html) to generate data similar to the simulation in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011). For exposition, we use the simple linear `dgp_type=\"dgp1\"`, with 150 units, 10 time periods per unit, and a true treatment effect of `theta=0.5`.\n", "\n", - "Documentation of the data generating process can be also found [API Reference](https://docs.doubleml.org/stable/api/datasets.html).\n", - "\n", - "We set `time_type=\"int\"` such that the time variable values will be integers. It's also possible to use `\"float\"` or `\"datetime\"` time variables with `DoubleMLPLPR`." + "We set `time_type=\"int\"` such that the time variable values will be integers. It's also possible to use `\"float\"` or `\"datetime\"` time variables with [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr)." ] }, { @@ -272,7 +270,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To create a corresponding `DoubleMLPanelData` object, we need to set `static_panel=True` and specify `id` and `time` columns." + "To create a corresponding [DoubleMLPanelData](https://docs.doubleml.org/stable/api/generated/doubleml.data.DoubleMLPanelData.html) object, we need to set `static_panel=True` and specify `id_col` and `time_col` columns." ] }, { @@ -295,21 +293,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The partially linear panel regression (PLPR) model extends the partially linear model to panel data by introducing fixed effects $\\alpha_i$.\n", + "The partially linear panel regression (PLPR) model extends the partially linear model to panel data by introducing fixed effects $\\alpha_i^*$.\n", "\n", - "Partialled-out PLPR (PO-PLPR) model:\n", + "The PLPR model takes the form\n", "\n", "\\begin{align*}\n", - " Y_{it} &= \\theta_0 D_{it} + g_0(X_{it}) + \\alpha_i + U_{it} \\\\\n", - " D_{it} &= m_0(X_{it}) + \\gamma_i + V_{it}\n", + " Y_{it} &= \\theta_0 D_{it} + g_1(X_{it}) + \\alpha_i^* + U_{it}, \\\\\n", + " D_{it} &= m_1(X_{it}) + \\gamma_i + V_{it},\n", "\\end{align*}\n", "\n", + "where\n", "- $Y_{it}$ outcome, $D_{it}$ treatment, $X_{it}$ covariates, $\\theta_0$ causal treatment effect\n", - "- $g_0$ and $m_0$ nuisance functions\n", - "- $\\alpha_i$, $\\gamma_i$ unobserved individual heterogeneity, correlated with covariates\n", + "- $g_1$ and $m_1$ nuisance functions\n", + "- $\\alpha_i^*$, $\\gamma_i$ unobserved individual heterogeneity, correlated with covariates\n", "- $U_{it}$, $V_{it}$ error terms\n", "\n", - "Further note $\\mathbb{E}[U_{it} \\mid D_{it}, X_{it}, \\alpha_i] = 0$, but $\\mathbb{E}[\\alpha_i \\mid D_{it}, X_{it}] \\neq 0$, and $\\mathbb{E}[V_{it} \\mid X_{it}, \\gamma_i]=0$\n" + "Further note $\\mathbb{E}[U_{it} \\mid D_{it}, X_{it}, \\alpha_i^*] = 0$ and $\\mathbb{E}[V_{it} \\mid X_{it}, \\gamma_i]=0$, but $\\mathbb{E}[\\alpha_i^* \\mid D_{it}, X_{it}] \\neq 0$.\n", + "\n", + "Alternatively we can write the partialling-out PLPR as \n", + "\n", + "\\begin{align*}\n", + " Y_{it} &= \\theta_0 V_{it} + \\ell_1(X_{it}) + \\alpha_i + U_{it}, \\\\\n", + " V_{it} &= D_{it} - m_1(X_{it}) - \\gamma_i,\n", + "\\end{align*}\n", + "\n", + "with nuisance function $\\ell_1$ and fixed effect $\\alpha_i$." ] }, { @@ -318,10 +326,7 @@ "source": [ "#### Assumptions\n", "\n", - "Define potential outcomes $Y_{it}(d)$ for individual $i$ at wave $t$, where realizations are linked to the observed outcome by the consistency assumption that\n", - "$Y_{it}(d_{it}) = Y_{it}$. \n", - "\n", - "$\\xi_i$ are time-invariant heterogeneity terms influencing outcome and treatment. Further, $L_{t-1}(W_i) = \\{ W_{i1}, \\dots, W_{it-1} \\}$, lags of a random variable $W_{it}$ at wave $t$.\n", + "Define $\\xi_i$ as time-invariant heterogeneity terms influencing outcome and treatment and $L_{t-1}(W_i) = \\{ W_{i1}, \\dots, W_{it-1} \\}$ as lags of a random variable $W_{it}$ at wave $t$.\n", "\n", "- No feedback to predictors\n", "$$ X_{it} \\perp L_{t-1} (Y_i, D_i) \\mid L_{t-1} (X_i), \\xi_i $$\n", @@ -344,11 +349,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To estimate the causal effect, we can create a `DoubleMLPLPR` object. \n", + "To estimate the causal effect, we can create a [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object. \n", "\n", - "The model described in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) uses block-k-fold cross-fitting, where the entire time series of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit which is common with panel data. Furthermore, cluster robust standard error are employed. `DoubleMLPLPR` implements both aspects by using `id_col` as the cluster variable.\n", + "The model described in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) uses block-k-fold cross-fitting, where the entire time series of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit which is common with panel data. Furthermore, cluster robust standard error are employed. [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) implements both aspects by using `id_col` as the cluster variable.\n", "\n", - "[Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) describes multiple estimation approaches, which can be set with the `approach` parameter. Depending on the type of `approach`, different data transformation are performed along the way." + "[Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) describes multiple estimation approaches, which can be set with the `approach` parameter. Depending on the type of `approach`, different data transformations are performed along the way." ] }, { @@ -359,14 +364,12 @@ "\n", "#### Correlated Random Effect\n", "\n", - "The Correlated random effect approaches `cre_general` and `cre_normal`\n", - "\n", "`cre_general` approach:\n", "\n", - "- Learning $g_0$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$\n", - "- First learning $\\tilde{m}_0({\\cdot})$ from $\\{ D_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$ with prediction $\\hat{m}_{0it} = \\tilde{m}_0 (X_{it}, \\bar{X}_i) $\n", - " - Calculate $\\hat{\\bar{m}}_i = T^{-1} \\sum_{t=1}^T \\hat{m}_{0it} $\n", - " - Calculate final nuisance part as $ \\hat{m}^*_0 (X_{it}, \\bar{X}_i, \\bar{D}_i) = \\hat{m}_{0it} + \\bar{D}_i - \\hat{\\bar{m}}_i $ " + "- Learning $\\ell_1$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$,\n", + "- First learning $\\tilde{m}_1$ from $\\{ D_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$, with predictions $\\hat{m}_{1,it} = \\tilde{m}_1 (X_{it}, \\bar{X}_i) $\n", + " - Calculate $\\hat{\\bar{m}}_i = T^{-1} \\sum_{t=1}^T \\hat{m}_{1,it} $,\n", + " - Calculate final nuisance part as $ \\hat{m}^*_1 (X_{it}, \\bar{X}_i, \\bar{D}_i) = \\hat{m}_{1,it} + \\bar{D}_i - \\hat{\\bar{m}}_i $. " ] }, { @@ -386,7 +389,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can look at the the transformed data using the `data_transform` property after the `DoubleMLPLPR` object was created." + "We can look at the the transformed data using the `data_transform` property after the [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object was created." ] }, { @@ -609,7 +612,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After fitting the model, we can print the `DoubleMLPLPR` object.\n", + "After fitting the model, we can print the [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object.\n", "\n", "The Data Summary corresponds to the transformed data. Additional Information at the end also includes a Pre-Transformation Data Summary." ] @@ -680,9 +683,9 @@ "source": [ "`cre_normal` approach:\n", "\n", - "Under the assumption that the conditional distribution $ D_{i1}, \\dots, D_{iT} \\mid X_{i1}, \\dots X_{iT} $ is multivariate normal\n", - "- Learn $g_0$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$\n", - "- Learn $m^*_{0}$ from $\\{ D_{it}, X_{it}, \\bar{X}_i, \\bar{D}_i: t=1,\\dots, T \\}_{i=1}^N$" + "Under the assumption that the conditional distribution $ D_{i1}, \\dots, D_{iT} \\mid X_{i1}, \\dots X_{iT} $ is multivariate normal (see [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) for further details):\n", + "- Learn $\\ell_1$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$,\n", + "- Learn $m^*_{1}$ from $\\{ D_{it}, X_{it}, \\bar{X}_i, \\bar{D}_i: t=1,\\dots, T \\}_{i=1}^N$." ] }, { @@ -746,10 +749,10 @@ "\n", "`fd_exact` approach:\n", "\n", - "Consider FD transformation $Q(Y_{it})= Y_{it} - Y_{it-1} $, under Assumptions from above, transformed nuisance function can be learnt as\n", + "Consider FD transformation $Q(Y_{it})= Y_{it} - Y_{it-1} $, under the assumptions from above, [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) show that $\\mathbb{E}[Y_{it}-Y_{it-1} | X_{it-1},X_{it}] =\\Delta \\ell_1 (X_{it-1}, X_{it})$ and $\\mathbb{E}[D_{it}-D_{it-1} | X_{it-1},X_{it}] =\\Delta m_1 (X_{it-1}, X_{it})$. Therefore, the transformed nuisance function can be learnt as\n", "\n", - "- $ \\Delta g_0 (X_{it-1}, X_{it}) $ from $ \\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $\n", - "- $ \\Delta m_0 (X_{it-1}, X_{it}) $ from $ \\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $\n" + "- $ \\Delta \\ell_1 (X_{it-1}, X_{it}) $ from $ \\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $,\n", + "- $ \\Delta m_1 (X_{it-1}, X_{it}) $ from $ \\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $.\n" ] }, { @@ -994,14 +997,20 @@ "source": [ "`wg_approx` approach:\n", "\n", - "For WG transformation $Q(X_{it})= X_{it} - \\bar{X}_{i} $, where $ \\bar{X}_{i} = T^{-1} \\sum_{t=1}^T X_{it} $. Approximate model\n", + "For WG transformation $Q(X_{it})= X_{it} - \\bar{X}_{i} $, where $ \\bar{X}_{i} = T^{-1} \\sum_{t=1}^T X_{it} $. Approximate the model as\n", "\\begin{align*}\n", - " Q(Y_{it}) &\\approx \\theta_0 Q(D_{it}) + g_0 (Q(X_{it})) + Q(U_{it}) \\\\\n", - " Q(D_{it}) &\\approx m_0 (Q(X_{it})) + Q(V_{it})\n", + " Q(Y_{it}) &\\approx \\theta_0 Q(D_{it}) + g_1 (Q(X_{it})) + Q(U_{it}), \\\\\n", + " Q(D_{it}) &\\approx m_1 (Q(X_{it})) + Q(V_{it}).\n", "\\end{align*}\n", "\n", - "- $g_0$ can be learnt from transformed data $ \\{ Q(Y_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $\n", - "- $m_0$ can be learnt from transformed data $ \\{ Q(D_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $" + "Similarly for the partialling-out PLPR\n", + "\n", + "$$\n", + "Q(Y_{it}) \\approx \\theta_0 Q(V_{it}) + \\ell_1 (Q(X_{it})) + Q(U_{it}).\n", + "$$\n", + "\n", + "- $\\ell_1$ can be learnt from transformed data $ \\{ Q(Y_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $,\n", + "- $m_1$ can be learnt from transformed data $ \\{ Q(D_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $." ] }, { @@ -1519,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -1537,7 +1546,7 @@ "source": [ "We can apply the polynomial and intercation transformation for specific sets of covariates. For example, for the `fd_exact` approach, we can apply it to the original $X_{it}$ and lags $X_{it-1}$ seperately using `ColumnTransformer`.\n", "\n", - "To achieve this, we pass need to pass the corresponding indices for these two sets. `DoubleMLPLPR` stacks sets $X_{it}$ and $X_{it-1}$ column-wise. Given our example data has 30 covariates, this means that the first 30 features in the nuisance estimation correspond to the original $X_{it}$, and the last 30 correspond to lags $X_{it-1}$. Therefore we define the indices `indices_x` and `indices_x_tr` as below." + "To achieve this, we pass need to pass the corresponding indices for these two sets. [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) stacks sets $X_{it}$ and $X_{it-1}$ column-wise. Given our example data has 30 covariates, this means that the first 30 features in the nuisance estimation correspond to the original $X_{it}$, and the last 30 correspond to lags $X_{it-1}$. Therefore we define the indices `indices_x` and `indices_x_tr` as below." ] }, { @@ -3448,7 +3457,7 @@ "source": [ "We can also look at the resulting features.\n", "\n", - "**Remark**: Note, however, that the feature names here refer only to the corresponding `x_cols` indices, not the column names from the `pd.DataFrame` because `DoubleML` uses `np.array`'s for fitting the model. Therefore the difference to the names from `x_cols_to_pre`." + "**Remark**: Note, however, that the feature names here refer only to the corresponding `x_cols` indices, not the column names from the `pd.DataFrame` because [DoubleML](https://docs.doubleml.org/stable/index.html) uses `np.array`'s for fitting the model. Therefore the difference to the names from `x_cols_to_pre`." ] }, { @@ -3495,10 +3504,13 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ + "dim_x = 30\n", + "theta = 0.5\n", + "\n", "np.random.seed(11)\n", "data_tune = make_plpr_CP2025(num_id=4000, num_t=10, dim_x=dim_x, theta=theta, dgp_type=\"dgp3\")\n", "dml_data_tune = DoubleMLPanelData(data_tune, y_col=\"y\", d_cols=\"d\", t_col=\"time\", id_col=\"id\", static_panel=True)\n", @@ -3507,7 +3519,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -3535,15 +3547,15 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Best trial: 41. Best value: -1.45877: 100%|██████████| 100/100 [02:29<00:00, 1.49s/it]\n", - "Best trial: 97. Best value: -1.19917: 100%|██████████| 100/100 [02:30<00:00, 1.51s/it]\n" + "Best trial: 94. Best value: -1.45766: 100%|██████████| 100/100 [03:22<00:00, 2.03s/it]\n", + "Best trial: 91. Best value: -1.2035: 100%|██████████| 100/100 [03:30<00:00, 2.11s/it]\n" ] }, { @@ -3578,12 +3590,12 @@ " \n", " \n", " d\n", - " 0.509103\n", - " 0.007696\n", - " 66.150849\n", + " 0.507695\n", + " 0.008154\n", + " 62.262137\n", " 0.0\n", - " 0.494019\n", - " 0.524187\n", + " 0.491713\n", + " 0.523677\n", " \n", " \n", "\n", @@ -3591,10 +3603,10 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 0.509103 0.007696 66.150849 0.0 0.494019 0.524187" + "d 0.507695 0.008154 62.262137 0.0 0.491713 0.523677" ] }, - "execution_count": 50, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -3611,17 +3623,24 @@ "plpr_tune_cre_general.summary" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0.509102" + ] + }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Best trial: 21. Best value: -1.46167: 100%|██████████| 100/100 [02:29<00:00, 1.50s/it]\n", - "Best trial: 85. Best value: -1.2188: 100%|██████████| 100/100 [02:32<00:00, 1.53s/it]\n" + "Best trial: 71. Best value: -1.46224: 100%|██████████| 100/100 [03:22<00:00, 2.03s/it]\n", + "Best trial: 43. Best value: -1.22011: 100%|██████████| 100/100 [03:20<00:00, 2.00s/it]\n" ] }, { @@ -3656,12 +3675,12 @@ " \n", " \n", " d\n", - " 0.470911\n", - " 0.010029\n", - " 46.953905\n", + " 0.472977\n", + " 0.010134\n", + " 46.670874\n", " 0.0\n", - " 0.451254\n", - " 0.490568\n", + " 0.453114\n", + " 0.492839\n", " \n", " \n", "\n", @@ -3669,10 +3688,10 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 0.470911 0.010029 46.953905 0.0 0.451254 0.490568" + "d 0.472977 0.010134 46.670874 0.0 0.453114 0.492839" ] }, - "execution_count": 51, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3691,15 +3710,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Best trial: 77. Best value: -1.75175: 100%|██████████| 100/100 [02:19<00:00, 1.40s/it]\n", - "Best trial: 52. Best value: -1.51454: 100%|██████████| 100/100 [02:26<00:00, 1.46s/it]\n" + "Best trial: 98. Best value: -1.75751: 100%|██████████| 100/100 [01:51<00:00, 1.11s/it]\n", + "Best trial: 90. Best value: -1.51545: 100%|██████████| 100/100 [02:03<00:00, 1.24s/it]\n" ] }, { @@ -3734,23 +3753,23 @@ " \n", " \n", " d_diff\n", - " 0.553526\n", - " 0.008697\n", - " 63.644385\n", + " 0.551595\n", + " 0.008651\n", + " 63.757531\n", " 0.0\n", - " 0.53648\n", - " 0.570572\n", + " 0.534638\n", + " 0.568551\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d_diff 0.553526 0.008697 63.644385 0.0 0.53648 0.570572" + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_diff 0.551595 0.008651 63.757531 0.0 0.534638 0.568551" ] }, - "execution_count": 49, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -3769,15 +3788,15 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Best trial: 52. Best value: -2.25599: 100%|██████████| 100/100 [01:11<00:00, 1.40it/s]\n", - "Best trial: 51. Best value: -1.63026: 100%|██████████| 100/100 [01:16<00:00, 1.30it/s]\n" + "Best trial: 91. Best value: -2.25528: 100%|██████████| 100/100 [01:26<00:00, 1.15it/s]\n", + "Best trial: 21. Best value: -1.62987: 100%|██████████| 100/100 [01:33<00:00, 1.07it/s]\n" ] }, { @@ -3812,12 +3831,12 @@ " \n", " \n", " d_demean\n", - " 1.136306\n", - " 0.005134\n", - " 221.308148\n", + " 1.137408\n", + " 0.004974\n", + " 228.656233\n", " 0.0\n", - " 1.126242\n", - " 1.146369\n", + " 1.127659\n", + " 1.147158\n", " \n", " \n", "\n", @@ -3825,10 +3844,10 @@ ], "text/plain": [ " coef std err t P>|t| 2.5 % 97.5 %\n", - "d_demean 1.136306 0.005134 221.308148 0.0 1.126242 1.146369" + "d_demean 1.137408 0.004974 228.656233 0.0 1.127659 1.147158" ] }, - "execution_count": 52, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3847,7 +3866,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -3857,15 +3876,15 @@ "True treatment effect: 0.5\n", "\n", " Model theta se ci_lower ci_upper\n", - "cre_general 0.509103 0.007696 0.494019 0.524187\n", - " cre_normal 0.470911 0.010029 0.451254 0.490568\n", - " fd 0.553526 0.008697 0.536480 0.570572\n", - " wg 1.136306 0.005134 1.126242 1.146369\n" + "cre_general 0.507695 0.008154 0.491713 0.523677\n", + " cre_normal 0.472977 0.010134 0.453114 0.492839\n", + " fd_exact 0.551595 0.008651 0.534638 0.568551\n", + " wg_approx 1.137408 0.004974 1.127659 1.147158\n" ] }, { "data": { - "image/png": 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0Av2xHUjTZfTHt3cQ4wkMngIPHgPPVhW4PPRAWo+laROBdt11V/d/YK0OTdPR9AwdMOngQQcWmjrh1ZMJFHqmr/JoqoRqlKitOuDR1I3AgwHvuWraUygdsIe+Fmpb4AGP6MAznPBA03F0EKPXSe+Z7kfTIcV7jqqzI5paVhGdZVBTTAKpvaq3EhpQagpS4PM9/fTT3dQVTYXSNBlNV9HUoMADzquvvtodsOt122WXXdwUm3DqyoTSAa4O0jU9UFNjwp36VxWV2VcD37NwXzfvsxM6ZSx0/1EtH00507Qwvc+BFx2kBhYlD6UpR6o9pCBM780hhxzi9mPVmaqKyu7jZe1XFdE+osAl8LI9ZT2GPtMKyjX9S9OfFL4o7Crr8x9K4YRen8BLOHXevKlYlQn1w6FgR1PMQt9773UJfe9D+zN9VhRmXnfddaXuwzv7X+h9hO776pfE28/D6VvUblFtstDH1fuiqZUVvR+aFq7AR32lajJpWwWfZW2nfiWUfjeE1nAK93dIWa/l9vZ/fb690E00rU9Tqr06YNoP1X59lgPbr9cynD46nPcFABIVNaUAII4o4NABdWiB2IqUNfKmvG+/K7M8tDB0OFSrRDV9dAB+//33u3pKOgOW6oOUVVg78Bvt7dFIA30zrTpZqhmjWkI64FeBdtUYqazynnNFdFCj0V16r1QDRaGGDtr07bvCn+2NPiiPDpoCa3dVhl4/jXpQDSUdMOoAWrVnNPpNr5Oepw7YVB9MYaFu1+gAvTc6aFPNpnCpvozq16jGjA5SjzrqKKsuldlXq7Kfhst7PxU6esXHQ+nAvTwaeaVRVq+++qobOaRwQrWgNHpGtYyqUyT7VaSPofpsGtGiEXzaDzUiTc9b9cG2F5Qp7DzssMOClmnEYXn1twJ5/WZoSB/p+69ROCqoXxYvUCmvP/P2H9UhUjhXltD2RqM/9h5X/WRo/afQkWVlUa01vX8aAaV6bDqxhNql99ALxapbuL8byqK6XPqdo8+fRgcrvNbvSYX2Vemjo/17EgDiCaEUAMQZFa3WqIyZM2cGTbUri850pD+w9a24NxJEVPxY4YlujyY9lkYnBR6I/fLLL+5/76BRgYdCGh2A64DVowOESCng0tQlXTS6QEVpdTYkhVLec1X4okAmkJZF67XQVCxNBVEYpuAt8MA5kDcCRwfKFY02KYvaq+ksGjUXOPLDmx4Y+HwUCmhKji4TJ050RcBVLFtBlffYKq6sUVW6aDTKySef7F47hUyhU4C2N1JAo7L0GmjKlkbJ1Dbhvm7eZ0cH2IEjL7SvBPLOzKfROlV5H719QdMHddFnVSGBQhsd+Ff2ucViH48Wnc1TlzFjxriwSfuOTqpw0003lRumaypV6JRBjfwMh4qVS3nhT1XovdMIrKq+996oIAXzVb2Pstrk9S3lBXDeOgrPq/K4OqmE2q5+LvB98kZ3lTcyK5B+N4SGieH8Dgln/w+lz7dGQ6mf89qvEFmfM4+Kpuv3YujrVNkvgQAAwZi+BwBxRt/I6w9rTcdSuBRKB9E6s5HoNOwSegYoBRNS1pnEInXvvfcGfUOs6zrgUiDifZusg5jAKTealqGRIlWl+wqdMqJv7jWqTNNQRLVOtEwHvd4y0dQpnVkpWq+F92154LfjCnk08iiQAjNNP9F7E3ogFM4363pv9bwDX2/R2eP0+nqjw1TrJZQ3MsJ7HRSiBVJdFNXxUjtUb6UyFCjowFQjEWqjcF837/977rknaL3Qz5Leb9W2Udha1sGrpveVR/WUdCAcehCskCtwHw1XrPbxaNTGU/2hQAqnFJ4Gtlv9XOhnQ1OiQqcPhhOaahSmpqUpyPf6omjQCE19QaCQPZTaHvo8Q+n90hnfHnroIVcTqTL7T3k0QlH7kEYthe5fXt+iM+5pX9MZEb1pjZV53LL6OdX002tRFvXvgbWxdIY+rV/WKNaKfods70sJ9W2aVhi43+hzqdF43u9Dr/2h/azO0hc6FVSf7Xnz5pU6U23ocwcAlK/2fUUJAIiIDiR0gKURLRr9pNOTq+aFgg+NNlBNH02r8EYV6NtgjazyppXpYEB/tGvaReg0mEjp4FDTv/SYKmKrg2FNGbvmmmtK6jPpwFihmE77rZpDGtGkejL6Rv+7776r0uNq1Ium/JxyyinuOWvaiUbDqHC39024Dmo0nU91fvQ6aKqZQj0FePoGXqeBj4YDDzzQHTjrNdCUJAUdGqERegCjA3DV0dLULR1IqV06qNI3+qpJVdZBbiBtp/dPI54U6ul568BL06E0JcUbCaEphJq+p9ddIwn0eisg0+vVp0+fkoNYjTbRSBXVNlKAoQNBbVPZ+jt6bXUJhw58vVExgRTW6VTq1SHc103vifYRvVYKPPW+Tp8+3dUACqVTwWvUmfZ5TV1UoKcwUFM2tR+WFQx6I0B0oK1gQ9toZJkOfrVfahpRZcVqH4+UpiaqePWpp57qRsQouNFnxAv4PApO9Pqpv1DArP3CO8nC9mgUjPoA9YkKQvRZUo00vdfqH6NJU9def/11N4JV/a7arLpF33//vWuH9jGN0Nke9X/6LCqY0/6jEUh63xTw6AQECkUqQ6OfFLLqi4v99tvP9bPqk3Q/CkLV/6v/UUinUEgnKdA+o0Lker20L+s+3njjjXIfQ89Xo6RUO0/9hEaCKgzVflxWyKX+Xc9RIygVPCrc1RTf0GmP4fwO2R5NR9RzUviokx6odpTCJk3PU53BwPZrn9NytVmvtfY1tSn0/dX7qH11yJAh7v3V51nvuZ6v9ikAQAVq+vR/AIDqoVOQDxs2zN+hQwd3umydAvyggw7yT548OejU4oWFhf7x48e703ynpaX527dv7x89enTQOpU9Dbp3Su7AU9nrdN46NfqiRYv8Rx11lD8zM9OdHlynaS8uLg7a/tFHH3WnI09PT/fvvvvu7lTn3uncK3rswNu0jeg03P/+97/93bp1c6+D2qGf77///lLbPffcc/4ePXq4x27atKn/7LPPDjpVeeBzCVVWG8vy+eef+w844AB3GvS2bdv6r7rqKv+7777rttXpwwPNmDHDnX7da/fee+/t3sOK2iKbNm1yp1TXY+i91Wuq9yTwFO/Tp0/3n3jiiW4d7Sf6/8wzzww6hf1DDz3kP+SQQ/zNmjVzr0unTp3c65mTk7Pd51nWflCWsp5D37593bZlXY444ohy70v7itb55ptvynxvvNPGb++xw3ndZOvWrf7LLrvMvS66jxNOOMH/xx9/BO17nlWrVrl9VZ8v3Wfr1q3d83j44YdLvV56DrJ27Vq3jT4Duv/s7Gx/r169/M8///x2X8/tvQ6R7uNVebyyntv2HmPx4sX+IUOGuP0sIyPDtfGwww7zf/DBB0HrLViwwO2X+hzpvnV/2+PtA95F973DDjv4jz/+eP/UqVNL9XleG9X3VbUv9PYn9amdO3d2n7HmzZv7DzzwQP+dd97p37ZtW1ifFfWbAwcOdPuN9p927dq5dr/44osVvgfqU8rqW15//XXXDr1+WVlZ/v3339//v//9L2idOXPm+E8++eSSz76e+2mnneb6je3RZ+WWW25x62s77W9vvvlmqdcz8HlPmDDBfT60/sEHH+yfN29e0H2G+zukotdS+5F+F3rPW5/bH3/8MWidDRs2+AcPHuzeq4YNG/r79+/v9je1PXQ/W7dunX/48OHuPdH7q31K6+jzG/j6v/DCCxV+JgAgESXpn4qCKwAAIqVRAvpGuaxvyQEAiUcjxTTCTSOYVMx9e/gdAgDxiZpSAAAAAAAAiDlCKQAAAAAAAMQcoRQAAAAAAABijppSAAAAAAAAiDlGSgEAAAAAACDmCKUAAAAAAAAQc6mWYHw+ny1fvtwaNWpkSUlJNd0cAAAAAACAuKJKUZs2bbK2bdtacnL546ESLpRSINW+ffuabgYAAAAAAEBc++OPP2yHHXYo9/aEC6U0Qsp7YbKysqyuj/pas2aNtWjRYrvJIwCgfPSlABA5+lIAiJwvjvrS3NxcNyDIy2DKk3ChlDdlT4FUPIRS+fn57nnU9R0WAGoKfSkARI6+FAAi54vDvrSisknx8SwBAAAAAABQpxBKAQAAAAAAIOYIpQAAAAAAABBzCVdTKlzFxcVWWFhotX2+qdqoOafxMt+0tkhLS7OUlJSabgYAAAAAAHGLUCqE3++3lStX2saNG60utFXB1KZNmyosHobKa9y4sbVu3ZrXFgAAAACAakAoFcILpFq2bGmZmZm1OpBQKFVUVGSpqam1up11jV7XLVu22OrVq931Nm3a1HSTAAAAAACIO4RSIVP2vECqWbNmVtsRSlWf+vXru/8VTGl/YCofAAAAAADRRSGiAF4NKY2QArz9oLbXFgMAAAAAoC4ilCoDo44g7AcAAAAAAFQfQikAAAAAAADEHKEUYu7VV1+1zp07uzpNl19+ebnLAAAAAABA/CKUigPJycluqll5l+uvv95qk3/+8592yimn2B9//GE33nhjucsi8fHHH7vnrsL1AAAAAACg9uHse3Fg+fLlJfWPnnvuORs7dqz9/PPPJbc3bNgw6Ix9OsugzthXE/Ly8twZ7fr3729t27YtdxkAAAAAAIhvjJSKA61bty65ZGdnu4DKu75gwQJr1KiRvfPOO7bvvvtaenq6zZgxw8477zwbMGBA0P1o2tyhhx5act3n89mtt95qHTt2tPr161u3bt3sxRdf3G5bCgoK7Morr7R27dpZgwYNrFevXm7Ukuh/tUUOP/xw187ylonaefDBB7vHbt++vV122WW2efPmoMe6+uqr3W16Xpr+9+ijj9pvv/1mhx12mFunSZMm7j71fAEAAAAAQO1BKJUgRo0aZbfddpv99NNPtvfee4e1jQKpJ554wh588EGbP3++jRgxws455xz75JNPyt1m+PDhNnPmTHv22Wftu+++s1NPPdWOPvpo+/XXX+3AAw8sGcH10ksv2YoVK8pdtmjRIrfdP/7xD3c/GgGmkEr37xk4cKD973//s3vuucc9r4ceesiNClNIpfsS3bfu8+67747wFQQAAAAAANHE9L0wrfpsqa2asbTC9TLbNbLOA7sFLVv4xDzbsmxThdu26rOjtTp4R6sON9xwg/Xr1y/s9TUK6ZZbbrEPPvjAevfu7ZbtvPPOLhhS+NO3b99S2yxdutT++9//uv+9aXgaNTVt2jS3XPfXsmVLt7xp06ZuJJeUtUyB2Nlnn11S9HyXXXZx4ZMe94EHHnCP8fzzz9v7779vRx55ZEn7PLov774bN25cxVcNAAAAAABUF0KpMBUXFFlhbkGF6xVlp5delrctrG31GNWlZ8+elVp/4cKFtmXLllJB1rZt26xHjx5lbvP999+7elW77rprqYCrWbNmlXr8efPmuRFSTz/9dFA9LE0pXLJkiXssnamvrHAMAAAAAADUfoRSYUpJT7W0rNKBU6jUhvXKXBbOtnqM6qL6TqFn7FPIE6iwsLDkZxUfl7feesvVhwqk+k1l0TYKimbNmuX+DxRYbD0cui+dkU91pELtuOOOLjQDAAAAAAB1F6FUmDStrqpT60Kn89UGLVq0sB9++CFo2dy5cy0tLc393LVrVxc+aZpcuKORNIJKI6V0Jj0VKI/EPvvsYz/++KMrXl6Wvfbay42aUn0rb/peoHr1/goH1R4AAAAAAFD7EEolKJ3p7o477nCFzFUz6qmnnnIhlTc1T2fEUz0oFTdX+NOnTx/Lycmxzz//3LKysmzQoEGl7lPT9lQHSgXIJ0yY4O5rzZo1Nn36dFdc/bjjjgu7fTqr3gEHHOAKmw8dOtSN9FJIpRpS9957r3Xo0MG1YciQIa7WlM4M+Pvvv7tA7LTTTrOddtrJnXXvzTfftGOPPdadwa+yo7UAAAAAAIi2ddMmuksgN5PJ73MDK3I08ygp2R3TBmp29Eh3iSecfS9B9e/f36677jq76qqrbL/99rNNmza5MCnQjTfe6NZR0fEuXbq4s+FpOl/Hjh3LvV8VNNf9XHHFFbbbbrvZgAED7JtvvnFT7ipDIZZGQf3yyy9u1JUCrrFjx5YUUBcVPD/llFPs4osvtt13392GDRtmmzdvdrdpyuH48ePdWQdbtWoVdNY+AAAAAABqSvHWXCvasCzoUrxxuRXnrDTLW+P+1/VS62zNtXiT5A8tLBTncnNzLTs724360YifQPn5+a6ItkKXjIwMq+301hUVFVlqamqpBBWRq2v7A4Cq0WhQjbLU2TpVbw8AUHn0pQBQ+ZFSvsJ88+Wt19H9dtZOsuSGTS05LaNOjZTaXvYSiOl7AAAAAAAAMaJgKb3dHrZ04nFmGmCyvbFCSUnm27LRdhj5ljXcq7/FG77GAAAAAAAAiJHizRvtj8n/+CuM8vu2v7Ju9/vd+tou3hBKAQAAAAAAxMjGzx83f8GWigMpj99n/m1bbOPnT1i8IZQCAAAAAACIUW3o9e9PrtK269+/56+z9MURQikAAAAAAIAYKM5bZ4WrF1VQ3LwMfr/brnizCqPHD0IpAAAAAACAGPDl50W2/dZNFk8IpQAAAAAAAGIgOaNhZNvXb2TxJLWmGxAPJn6yyCZ9ujhomeZ5+lRI3/yWZEmWnKQzOSYFrTPikJ1tZN9OMW4tAAAAAACoCSkNm1lay05WuGbxX2ffC1dSkqW12NlSGjS1eEIoFQW5+UW2LCe/StsBAAAAAIDEoMEqTftdaqueHlHpbZv2u6zUYJe6jul7UZCVkWrtsjOsWWaaVbR76Hatp/W1HWrOb7/95j7Qc+fOremmAAAAAAASROODBllSeqZZUpiRTFKyJdXLtMYHDbR4QygVBZqCN+W0brZxa5FG1G2Xbtd6Wp+pewAAAAAAJJaNn021pLQMM78vvA38Pre+tos3hFJRsHFroZ3y+LeufpTqSG2PV2dK62u7mlJYWHOPXd22bdtW000AAAAAAKBMxVtzzZe3rlLbaH1tF28IpaLg8W//sC3biisMpDxaT+s/8e0fUW2Hz+ez22+/3Tp37mzp6em244472s0331wyTe25556zvn37WkZGhj399NNumylTpliXLl3cst13393uv//+sB/viy++sO7du7tte/bsaa+++mqp6XA//PCDHXPMMdawYUNr1aqVnXvuubZ27dqS2w899FC77LLL7KqrrrKmTZta69at7frrrw96nI0bN9rQoUOtRYsWlpWVZYcffrjNmzev5Hatr3bouXTs2NG1R6ZNm2Z9+vSxxo0bW7Nmzez444+3RYsWRfQaAwAAAAAQiZT6WZbapJ27pGS3seT62WbJKcErJae45SmN2/y9bv0sizcUNYqQzrJ374wlVdp28owldmmfjlErVDZ69Gh75JFHbNKkSS6MWbFihS1YsKDk9lGjRtmECROsR48eJcHU2LFj7d5773XL5syZY8OGDbMGDRrYoEGDtvtYubm5dsIJJ9ixxx5rzzzzjP3+++92+eWXlwqTFCApUFKbtm7daldffbWddtpp9uGHH5as9/jjj9vIkSPtq6++spkzZ9p5551nBx10kPXr18/dfuqpp1r9+vXtnXfesezsbHvooYfsiCOOsF9++cUFWbJw4UJ76aWX7OWXX7aUlL8+zJs3b3b3u/fee1teXp57rieddJILzZKTyWMBAAAAALHX7OiR7hKaLRRtWmur//zNWu7QwVIbNY+7ouZlIZSK0Lot22zRui2V3k6DqrTd+i2F1qxBvYjbsWnTJrv77rtdwOQFSp06dXLhlEZKiUKjk08+uWSbcePGuZDKW6ZRRj/++KMLfSoKpRRE6QOiEEwBV9euXW3ZsmUu1PJ4Ydctt9xSsmzq1KnWvn17FyjtuuuubplCI7VFdtllF7fd9OnTXSg1Y8YM+/rrr2316tVu9JfceeedblTWiy++aBdccEHJlL0nnnjCjaby/OMf/whqsx5bt+s57rnnnlV+rQEAAAAAiKakpCRLadjMUpoWu/8TIZASQqkI5RUUR7T9poKiqIRSP/30kxUUFLgRROXRFDuPRhFpKtv5558fFCQVFRW50UgV+fnnn12Y5E2Vk/333z9oHU2x++ijj9zUvVB67MBQKlCbNm1cCOXdh0Y5afpdII26CpyKt9NOOwUFUvLrr7+60VEagaUpg5reKEuXLiWUAgAAAACghhFKRahhesi8z0pqlB6dt0DT2yqiaXkeBT2ikU69evUKWs+b/hYpPYam+P3nP/8pdZuCJ09aWlrQbUqEvQBJ96F1P/7441L3oVpRZT03jx5bYZWeY9u2bd19KoyiEDoAAAAAADWPUCpCzTLrWadmmbZ43RY3JS9cGoi3c7NMa5oZHMhUlaa9KZjStDfVcKqIio4rqFm8eLGdffbZlX683XbbzZ566ik3OsubVvfNN98ErbPPPvu4Ok8dOnSw1NSq7Wq6j5UrV7rtdT/hWrdunRvNpUDq4IMPdss0FRAAAAAAANQOVHuOkEb1DO/TsUrbRrPIuabRqYi4zmKn2kqa2vbll1/ao48+Wu4248ePt1tvvdXuueceV+Pp+++/t//+9782ceLECh/vrLPOciOPVNNJUwffffddV+tJvOd0ySWX2Pr16+3MM890gZXapPUGDx5sxcXhTXs88sgjrXfv3jZgwAB77733XH0snfXv2muvtW+//bbc7Zo0aeKm/D388MOuCLoKq6voOQAAAAAAqB0IpaJgUM/2llkvxZLDzJe0ntYf2LN9VNtx3XXX2RVXXOHqKHXp0sVOP/30ktpMZdGIqilTprggaq+99rK+ffvaY4895gqeVyQrK8veeOMNdya77t27u5BIjytenSmNxPr8889dAHXUUUe5x1CxdU27C/fsdwq43n77bTvkkENcmKU6VGeccYY7259Ge5VH9//ss8/arFmz3JS9ESNG2B133BHWYwIAAAAAgOqX5Nd5BxNIbm6uK+Sdk5PjgpVA+fn5tmTJEhfKBBbwDse7P6+246d8bX7zm8+//UAqyZLsraH721G7tbRIuFNGFhW5qW21oTL/008/7YIjvbbh1Liq7SLZHwDUHRr1qQC/ZcuWYQfmAIBg9KUAEDlfHPWl28teAtXtZ1lLTPxkkQ19fp41rp9qFUV8ul3rnf/8PLddXaZpgqrTpODm1VdfddMHTzvttLgIpAAAAAAAQPUilIqC3PwiW5aTb+u2FFZY7Fy3az2tr+1qq1tuucUaNmxY5uWYY45x66gA+TnnnOOmCmp63KmnnupqOAEAAAAAAFSEs+9FQVZGqrXLzig1tU7T+DSdT9P13LS9kCl22q62uvDCC92op7J4I6FUVF0XAAAAAACAyqrRVOTTTz91xadVjHrFihX2yiuvuLOslUfrqJC3zrqmM6pddtlldtddd1lNG9m3k7vEk6ZNm7oLAAAAAABA3E3f27x5s3Xr1s3uu+++sNYvKCiwFi1a2JgxY9x2AAAAAAAAqJtqdKSUahN59YnC0aFDB7v77rvdz1OnTq3GlgEAAAAAAKA6UegcAAAAAAAAMVd7K21Hiab86eLJzc11//t8PncJpOsqUO5dwpUz+y7LnXNP0LK/tvfpB1U4d/lfqULnPS6z7H0ur+IzC3ycv/9H9Hj7QVn7CoD44fX9fM4BoOroSwEgcr446kvDfQ5xH0rdeuutNn78+FLL16xZY/n5+UHLCgsL3QtXVFTkLuEqys+x4rxllW6btqvM44TSzlpcXOx+Dg28EDm9N9of1q1bZ2lpaTXdHADVRJ/znJwc16cmJzOAGACqgr4UACLni6O+dNOmTWGtF/eh1OjRo23kyJFBI6Xat2/vCqZnZWUFrauQSi9camqqu4QrNSPbUhq2M39Rvvny1ysu2s7aSZac0dSSUjPcdpV5nPIQmITn448/tsMPP9zWr19vjRs3rnB9vTfqCJo1a2YZGRkxaSOAmvnlr2Bfvxfq+i9/AKgp9KUAEDlfHPWl4R5Dx30olZ6e7i6h9AaHvsm6rh3Au4Sr8b4jrF6zPWzVaye60KmiUMp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" ] @@ -3883,7 +3902,7 @@ "ci_wg = plpr_tune_wg.confint()\n", "\n", "comparison_data = {\n", - " \"Model\": [\"cre_general\", \"cre_normal\", \"fd\", \"wg\"],\n", + " \"Model\": [\"cre_general\", \"cre_normal\", \"fd_exact\", \"wg_approx\"],\n", " \"theta\": [plpr_tune_cre_general.coef[0], plpr_tune_cre_normal.coef[0], plpr_tune_fd.coef[0], plpr_tune_wg.coef[0]],\n", " \"se\": [plpr_tune_cre_general.se[0], plpr_tune_cre_normal.se[0], plpr_tune_fd.se[0], plpr_tune_wg.se[0]],\n", " \"ci_lower\": [ci_cre_general.iloc[0, 0], ci_cre_normal.iloc[0, 0], ci_fd.iloc[0, 0], ci_wg.iloc[0, 0]],\n", @@ -3906,7 +3925,7 @@ "plt.axhline(y=theta, color=palette[4], linestyle='--',\n", " linewidth=2, label=\"True effect\", zorder=1)\n", "\n", - "plt.title(\"Comparison across ML models for First-Difference approach\")\n", + "plt.title(\"Comparison across DoubleMLPLPR approaches\")\n", "plt.ylabel(\"Coefficient Value\")\n", "plt.xticks(range(4), df_comparison[\"Model\"], rotation=15, ha=\"right\")\n", "plt.legend()\n", @@ -3941,7 +3960,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.13.9" }, "orig_nbformat": 4 }, diff --git a/doc/guide/data/panel_data.rst b/doc/guide/data/panel_data.rst index 6c69a0f4..0b6f7e26 100644 --- a/doc/guide/data/panel_data.rst +++ b/doc/guide/data/panel_data.rst @@ -1,4 +1,4 @@ -The ``DoubleMLPanelData`` class serves as data-backend for :ref:`DiD models ` and can be initialized from a dataframe. +The ``DoubleMLPanelData`` class serves as data-backend for :ref:`DiD models `, as well as the :ref:`DoubleMLPLPR model `, and can be initialized from a dataframe. The class is a subclass of :ref:`DoubleMLData ` and inherits all methods and attributes. Furthermore, it provides additional methods and attributes to handle panel data. @@ -7,6 +7,7 @@ Key arguments * ``id_col``: column to with unique identifiers for each unit * ``t_col``: column to specify the time periods of the observation +* ``static_panel``: Indicates whether the data model corresponds to a static panel data approach (``True``, used for the ``DoubleMLPLPR`` model) or to staggered adoption panel data (``False``, for :ref:`DiD models `) which is the default option. * ``datetime_unit``: unit of the time periods (e.g. 'Y', 'M', 'D', 'h', 'm', 's') .. note:: @@ -39,3 +40,29 @@ Example usage ) print(dml_data) + + +.. tab-set:: + + .. tab-item:: Python + :sync: py + + .. ipython:: python + + import numpy as np + import doubleml as dml + from doubleml.plm.datasets import make_plpr_CP2025 + + np.random.seed(42) + df = make_plpr_CP2025(num_id=100, num_t=5, x_dim=5) + dml_data = dml.data.DoubleMLPanelData( + df, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["x1", "x2", "x3", "x4", "x5"], + static_panel=True + ) + + print(dml_data) diff --git a/doc/guide/models/plm/plpr.rst b/doc/guide/models/plm/plpr.rst index 67e0260a..660b72ee 100644 --- a/doc/guide/models/plm/plpr.rst +++ b/doc/guide/models/plm/plpr.rst @@ -1,12 +1,183 @@ -**Partially linear panel regression (PLPR)** models by `Clarke and Polselli (2025) ` take the form +Suppose a panel study observes each of :math:`N` individuals over :math:`T` time periods (or waves). +For each individual :math:`i=1,\dots,N` and each period :math:`t=1,\dots,T`, the data consists of the +triple :math:`(Y_it, D_it, X_it)`. Let :math:`\{(Y_it, D_it, X_it) : t = 1, \dots , T\}_{i=1}^N` +denote :math:`N` independent and identically distributed (iid) random vectors, where each vector +corresponds to individual :math:`i` observed across all T waves. + +.. note:: + The notation and identifying assumptions are based on `Clarke and Polselli (2025) `_, with some small adjustments to better fit into the general package documentation conventions, sometimes slightly abusing notation. + See also the R package `xtdml `_ implementation and corresponding reference `Polselli (2025) `_ for further details. + +**Partially linear panel regression (PLPR)** models `(Clarke and Polselli, 2025) `_ take the form .. math:: - Y_{it} = \theta_0 D_{it} + g_0(X_{it}) + \alpha_i + U_{it}, & &\mathbb{E}(U_{it} | D_{it}, X_{it}, \alpha_i) = 0, + Y_{it} = \theta_0 D_{it} + g_1(X_{it}) + \alpha_i^* + U_{it}, & &\mathbb{E}(U_{it} | D_{it}, X_{it}, \alpha_i^*) = 0, - D_{it} = m_0(X_{it}) + \gamma_i + V_{it}, & &\mathbb{E}(V_{it} | X_{it}, \gamma_i) = 0, + D_{it} = m_1(X_{it}) + \gamma_i + V_{it}, & &\mathbb{E}(V_{it} | X_{it}, \gamma_i) = 0, where :math:`Y_{it}` is the outcome variable and :math:`D_{it}` is the policy variable of interest. -The high-dimensional vector :math:`X_{it} = (X_{it,1}, \ldots, X_{it,p})` consists of other confounding covariates. -:math:`\alpha_i` and :math:`\gamma_i` represent unobserved individual heterogeneity terms, correlated with the covariates -and :math:`U_{it}` and :math:`V_{it}` are stochastic errors. \ No newline at end of file +Further note that :math:`\mathbb{E}[\alpha_i^* | D_{it}, X_{it}] \neq 0`. The high-dimensional +vector :math:`X_{it} = (X_{it,1}, \ldots, X_{it,p})` consists of other confounding covariates. +:math:`\alpha_i^*` and :math:`\gamma_i` represent unobserved individual heterogeneity terms, +correlated with the covariates. :math:`U_{it}` and :math:`V_{it}` are stochastic errors. + +Alternatively one can write the *partialling-out* PLPR model as + +.. math:: + + Y_{it} = \theta_0 V_{it} + \ell_1(X_{it}) + \alpha_i + U_{it}, + + V_{it} = D_{it} - m_1(X_{it}) - \gamma_i, + +where :math:`\alpha_i` is a fixed effect. + +To account for the presence of unobserved heterogeneity, `Clarke and Polselli (2025) `_ +use different panel data approaches, under the following assumptions. + +Define potential outcomes :math:`Y_{it}(d)` for individual :math:`i` at wave :math:`t`, where realizations are +linked to the observed outcome by the consistency assumption :math:`Y_{it}(d_{it}) = Y_{it}`. + +:math:`\xi_i` represent time-invariant heterogeneity terms influencing outcome and treatment. +Further, define :math:`L_{t-1}(W_i) = \{W_{i1}, \dots, W_{it-1}\}` as lags of a random variable :math:`W_{it}` +at wave :math:`t`. + +Assumptions `(Clarke and Polselli, 2025) `_: + +- **No feedback to predictors** + :math:`X_{it} \perp L_{t-1} (Y_i, D_i) | L_{t-1} (X_i), \xi_i` + +- **Static panel** + :math:`Y_{it}, D_{it} \perp L_{t-1} (Y_i, X_i, D_i) | X_{it}, \xi_i` + +- **Selection on observables and omitted time-invariant variables** + :math:`Y_{it} (.) \perp D_{it} | X_{it}, \xi_i` + +- **Homogeneity and linearity of the treatment effect** + :math:`\mathbb{E} [Y_{it}(d) - Y_{it}(0) | X_{it}, \xi_i] = d \theta_0` + +- **Additive Separability** + :math:`\mathbb{E} [Y_{it}(0) | X_{it}, \xi_i] &= g_1(X_{it}) + \alpha^*_i, & &\alpha^*_i = \alpha^*(\xi_i)`, + + :math:`\mathbb{E} [D_{it} | X_{it}, \xi_i] &= m_1(X_{it}) + \gamma_i, & &\gamma_i = \gamma(\xi_i)` + + +**Correlated Random Effect (CRE) Approaches** + +These approaches convert the fixed-effects model into a random-effects specification using the +Mundlak device `(Mundlak, 1978) `_. + +Given the set of assumptions, the PLPR model under the CRE approaches take the form + +.. math:: + + Y_{it} = \theta_0 D_{it} + \tilde{g}_1 (X_{it}, \bar{X}_i) + a_i + U_{it}, + + D_{it} = \tilde{m}_1(X_{it}, \bar{X}_i) + c_i + V_{it}. + +For the *partialling-out* PLPR + +.. math:: + + Y_{it} = \theta_0 V_{it} + \tilde{\ell}_1(X_{it}, \bar{X}_i) + a_i + U_{it}, + + V_{it} = D_{it} - \tilde{m}_1(X_{it}, \bar{X}_i) - c_i, + +where :math:`a_i`, :math:`c_i` are random effects and covariate unit means +:math:`\bar{X}_i = T^{-1} \sum_{t=1}^{T} X_{it}`. + +**Transformation Approaches** + +These approaches remove individual heterogeneity from the model by transforming the data. +For some random variable :math:`W_{it}`, define the First-Difference (FD) transformation +:math:`Q(W_{it}) = W_{it} - W_{it-1}` (for :math:`t=2, \dots, T`), and the Within-Group (WG) +transformation :math:`Q(W_{it}) = X_{it} - \bar{X}_{i}`, where :math:`\bar{W}_{i} = T^{-1} \sum_{t=1}^T W_{it}`. + +The PLPR model under the transformation approaches takes the form + +.. math:: + + Q(Y_{it}) = \theta_0 Q(D_{it}) + Q(g_1(X_{it})) + Q(U_{it}), + + Q(D_{it}) = Q(m_1(X_{it})) + Q(V_{it}). + +For the *partialling-out* PLPR + +.. math:: + + Q(Y_{it}) = \theta_0 Q(V_{it}) + Q(\ell_1(X_{it})) + Q(U_{it}), + + Q(V_{it}) = Q(D_{it}) - Q(m_1(X_{it})). + +These transformations remove the fixed effect terms, as :math:`Q(\alpha_i^*)=Q(\alpha_i)=Q(\gamma_i)=0`. + +**Implementation** + +``DoubleMLPLPR`` implements the estimation and requires :ref:`DoubleMLPanelData ` with parameter ``static_panel=True`` as input. +Unit identifier and time period columns are set with ``id_col`` and ``t_col`` in :ref:`DoubleMLPanelData `. + +The model described in `Clarke and Polselli (2025) `_ uses block-k-fold sample splitting, where the entire time series +of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit, which is often the case for panel data. Furthermore, +cluster robust standard error are employed. ``DoubleMLPLPR`` implements both of these aspects by using ``id_col`` as the cluster variable internally, see the example notebook +`Python: Cluster Robust Double Machine Learning `_. + +The ``DoubleMLPLPR`` model inlcudes four different estimation approaches. The first two are correlated random effects (CRE) variants, the latter +two are transformation approaches. This can be selected with the ``approach`` parameter. + +``approach='cre_general'``: + +- Learn :math:`\tilde{\ell}_1 (X_{it}, \bar{X}_i)` from :math:`\{ Y_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N`, + +- First learn :math:`\tilde{m}_1(X_{it}, \bar{X}_i)` from :math:`\{ D_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N`, with predictions :math:`\hat{m}_{1,it} = \tilde{m}_1 (X_{it}, \bar{X}_i)` + + - Calculate :math:`\hat{\bar{m}}_i = T^{-1} \sum_{t=1}^T \hat{m}_{1,it}`, + + - Calculate final nuisance part as :math:`\hat{m}^*_1 (X_{it}, \bar{X}_i, \bar{D}_i) = \hat{m}_{1,it} + \bar{D}_i - \hat{\bar{m}}_i`, + + where :math:`\hat{m}^*_1 (X_{it}, \bar{X}_i, \bar{D}_i) = \mathbb{E}[D_{it} | X_{it}, \bar{X}_i] + c_i`. + +- :math:`g_1` can be learnt iteratively from :math:`\{ Y_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N` using estimates for :math:`\tilde{\ell}_1, \tilde{m}_1`. + +``approach='cre_normal'`` + +Under the assumption that the conditional distribution :math:`D_{i1}, \dots, D_{iT} | X_{i1}, \dots X_{iT}` is multivariate normal (see `Clarke and Polselli (2025) `_ for further details): + +- Learn :math:`\tilde{\ell}_1 (X_{it}, \bar{X}_i)` from :math:`\{ Y_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N`, + +- Learn :math:`m^*_{1}` from :math:`\{ D_{it}, X_{it}, \bar{X}_i, \bar{D}_i: t=1,\dots, T \}_{i=1}^N`, + +- :math:`g_1` can be learnt iteratively from :math:`\{ Y_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N` using estimates for :math:`\tilde{\ell}_1, \tilde{m}_1`. + +``approach='fd_exact'`` + +Consider First-Difference (FD) transformation :math:`Q(W_{it})= W_{it} - W_{it-1}`, under the assumptions from above, +`Clarke and Polselli (2025) `_ show that :math:`\mathbb{E}[Y_{it}-Y_{it-1} | X_{it-1},X_{it}] =\Delta \ell_1 (X_{it-1}, X_{it})` and +:math:`\mathbb{E}[D_{it}-D_{it-1} | X_{it-1},X_{it}] =\Delta m_1 (X_{it-1}, X_{it})`. Therefore, the transformed nuisance function can be learnt as + +- Learn :math:`\Delta \ell_1 (X_{it-1}, X_{it})` from :math:`\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \dots , T \}_{i=1}^N`, + +- Learn :math:`\Delta m_1 (X_{it-1}, X_{it})` from :math:`\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \dots , T \}_{i=1}^N`, + +- :math:`\Delta g_1 (X_{it-1}, X_{it})` can be learnt iteratively from :math:`\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \dots , T \}_{i=1}^N` using estimates for :math:`\Delta \ell_1, \Delta m_1`. + +``approach='wg_approx'`` + +For the Within-Group (WG) transformation :math:`Q(W_{it})= W_{it} - \bar{W}_{i}`, where :math:`\bar{W}_{i} = T^{-1} \sum_{t=1}^T W_{it}`. +Approximating the model gives + +.. math:: + \begin{align*} + Q(Y_{it}) &\approx \theta_0 Q(D_{it}) + g_1 (Q(X_{it})) + Q(U_{it}), \\ + Q(D_{it}) &\approx m_1 (Q(X_{it})) + Q(V_{it}). + \end{align*} + +Similarly, + +.. math:: + Q(Y_{it}) &\approx \theta_0 Q(V_{it}) + \ell_1 (Q(X_{it})) + Q(U_{it}). + +- Learn :math:`\ell_1` from transformed data :math:`\{ Q(Y_{it}), Q(X_{it}) : t=1,\dots,T \}_{i=1}^N`, + +- Learn :math:`m_1` from transformed data :math:`\{ Q(D_{it}), Q(X_{it}) : t=1,\dots,T \}_{i=1}^N`, + +- :math:`g_1` can be learnt iteratively from :math:`\{ Q(Y_{it}), Q(X_{it}) : t=1,\dots,T \}_{i=1}^N`, using estimates for :math:`\ell_1, m_1`. diff --git a/doc/guide/scores/plm/plm_scores.inc b/doc/guide/scores/plm/plm_scores.inc index 7b6ad0c8..a1122ef1 100644 --- a/doc/guide/scores/plm/plm_scores.inc +++ b/doc/guide/scores/plm/plm_scores.inc @@ -14,6 +14,13 @@ Logistic partial linear regression (LPLR) .. include:: /guide/scores/plm/lplr_score.rst +.. _plpr-score: + +Partially linear panel regression (PLPR) +======================================== + +.. include:: /guide/scores/plm/plpr_score.rst + .. _pliv-score: Partially linear IV regression model (PLIV) diff --git a/doc/guide/scores/plm/plpr_score.rst b/doc/guide/scores/plm/plpr_score.rst new file mode 100644 index 00000000..b9b0c2c5 --- /dev/null +++ b/doc/guide/scores/plm/plpr_score.rst @@ -0,0 +1,111 @@ +For the PLPR model implemented in ``DoubleMLPLPR`` one can choose between +``score='partialling out'`` and ``score='IV-type'``. + +``score='partialling out'`` implements the score function: + +For correlated random effect (cre) approaches ``approach='cre_general'`` and ``approach='cre_normal'`` + +.. math:: + + \psi(W_{it}; \theta, \eta) &:= [Y_{it} - \tilde{\ell}(X_{it},\bar{X}_i) - \theta (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i)] [D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i] + + &= - (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) \theta + (Y_{it} - \tilde{\ell}(X_{it},\bar{X}_i)) (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) + + &= \psi_a(W_{it}; \eta) \theta + \psi_b(W_{it}; \eta) + +with :math:`\eta=(\tilde{\ell},\tilde{m})`, where + +.. math:: + + \tilde{\ell}_0(X_{it},\bar{X}_i) &:= \mathbb{E}[Y_{it} \mid X_{it}, \bar{X}_i] = \theta_0\mathbb{E}[D_{it} \mid X_{it}, \bar{X}_i] + g(X_{it}, \bar{X}_i), + + \tilde{m}_0(X_{it},\bar{X}_i) + c_i &:= \mathbb{E}[D_{it} \mid X_{it}, \bar{X}_i]. + +The components of the linear score are + +.. math:: + + \psi_a(W_{it}; \eta) &= - (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i), + + \psi_b(_{it}W; \eta) &= (Y_{it} - \tilde{\ell}(X_{it},\bar{X}_i)) (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i). + + +For transformation approaches ``approach='fd_exact'`` and ``approach='wg_approx'``, where :math:`Q(W_{it})` indicates a transformated variable :math:`W_{it}` + +.. math:: + + \psi(Q(W_{it}); \theta, \eta) &:= [Q(Y_{it}) - Q(\ell(X_{it})) - \theta (Q(D_{it}) - Q(m(X_{it})))] [Q(D_{it}) - Q(m(X_{it}))] + + &= - (Q(D_{it}) - Q(m(X_{it}))) (Q(D_{it}) - Q(m(X_{it}))) \theta + (Q(Y_{it}) - Q(\ell(X_{it}))) (Q(D_{it}) - Q(m(X_{it}))) + + &= \psi_a(Q(W_{it}); \eta) \theta + \psi_b(Q(W_{it}); \eta) + +with :math:`\eta=(\ell,m)`, where + +.. math:: + + Q(\ell_0(X)) &:= \mathbb{E}[Q(Y_{it}) \mid Q(X_{it})] = \theta_0\mathbb{E}[Q(D_{it}) \mid Q(X_{it})] + Q(g(X_{it})), + + Q(m_0(X)) &:= \mathbb{E}[Q(D_{it}) \mid Q(X_{it})]. + +The components of the linear score are + +.. math:: + + \psi_a(Q(W_{it}); \eta) &= - (Q(D_{it}) - Q(m(X_{it}))) (Q(D_{it}) - Q(m(X_{it}))), + + \psi_b(Q(W_{it}); \eta) &= Q(Y_{it}) - Q(\ell(X_{it})) (Q(D_{it}) - Q(m(X_{it}))). + +``score='IV-type'`` implements the score function: + +For correlated random effect (cre) approaches ``approach='cre_general'`` and ``approach='cre_normal'`` + +.. math:: + + \psi(W_{it}; \theta, \eta) &:= [Y_{it} - D_{it} \theta - \tilde{g}(X_{it},\bar{X}_i)] [D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i] + + &= - D_{it} (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) \theta + (Y_{it} - \tilde{g}(X_{it},\bar{X}_i)) (D_{it} - \tilde{m}(X_{it},\bar{X}_i) - c_i) + + &= \psi_a(W_{it}; \eta) \theta + \psi_b(W_{it}; \eta) + +with :math:`\eta=(\tilde{g},\tilde{m})`, where + +.. math:: + + \tilde{g}_0(X_{it},\bar{X}_i) &:= \mathbb{E}[Y_{it} - D_{it} \theta_0 \mid X_{it},\bar{X}_i], + + \tilde{m}_0(X_{it},\bar{X}_i) + c_i &:= \mathbb{E}[D_{it} \mid X_{it}, \bar{X}_i]. + +The components of the linear score are + +.. math:: + + \psi_a(W_{it}; \eta) &= - D_{it} (D_{it} - \tilde{m}(X_{it},\bar{X}_i)), + + \psi_b(W_{it}; \eta) &= (Y_{it} - \tilde{g}(X_{it},\bar{X}_i)) (D_{it} - \tilde{m}(X_{it},\bar{X}_i)). + +For transformation scores ``approach='fd_exact'`` and ``approach='wg_approx'``, where :math:`Q(W_{it})` indicates a transformated variable :math:`W_{it}` + +.. math:: + + \psi(Q(W_{it}); \theta, \eta) &:= [Q(Y_{it}) - Q(D_{it}) \theta - Q(g(X_{it}))] [Q(D_{it}) - Q(m(X_{it}))] + + &= - Q(D_{it}) (Q(D_{it}) - Q(m(X_{it}))) \theta + (Q(Y_{it}) - Q(g(X_{it}))) (Q(D_{it}) - Q(m(X_{it}))) + + &= \psi_a(Q(W_{it}); \eta) \theta + \psi_b(Q(W_{it}); \eta) + +with :math:`\eta=(g,m)`, where + +.. math:: + + Q(g_0(X_{it})) &:= \mathbb{E}[Q(Y_{it}) - Q(D_{it}) \theta_0 \mid Q(X_{it})], + + Q(m_0(X_{it})) &:= \mathbb{E}[Q(D_{it}) \mid Q(X_{it})]. + +The components of the linear score are + +.. math:: + + \psi_a(Q(W_{it}); \eta) &= - Q(D_{it}) (Q(D_{it}) - Q(m(X_{it}))), + + \psi_b(Q(W_{it}); \eta) &= (Q(Y_{it}) - Q(g(X_{it}))) (Q(D_{it}) - Q(m(X_{it}))). \ No newline at end of file diff --git a/doc/index.rst b/doc/index.rst index 742a9cf7..bf226f2f 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -263,7 +263,7 @@ Acknowledgements ---------------- Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research -Foundation) is acknowledged – Project Number 431701914 and Grant GRK 2805/1. +Foundation) is acknowledged – Project Number 431701914, Grant GRK 2805/1 and Project Number 530859036. References ---------- From b8bb967946791d211ea30995ea2e836d991c7bf8 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 20:35:16 +0000 Subject: [PATCH 05/16] execute rdflex nb --- doc/examples/py_double_ml_rdflex.ipynb | 6610 +++++++++++++++++++++++- 1 file changed, 6578 insertions(+), 32 deletions(-) diff --git a/doc/examples/py_double_ml_rdflex.ipynb b/doc/examples/py_double_ml_rdflex.ipynb index 0d7b09e6..06abf4a0 100644 --- a/doc/examples/py_double_ml_rdflex.ipynb +++ b/doc/examples/py_double_ml_rdflex.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,9 +70,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.scatter(\n", " x=df['score'],\n", @@ -136,9 +3184,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Call: rdrobust\n", + "Number of Observations: 1000\n", + "Polynomial Order Est. (p): 1\n", + "Polynomial Order Bias (q): 2\n", + "Kernel: Triangular\n", + "Bandwidth Selection: mserd\n", + "Var-Cov Estimator: NN\n", + "\n", + " Left Right\n", + "------------------------------------------------\n", + "Number of Observations 493 507\n", + "Number of Unique Obs. 493 507\n", + "Number of Effective Obs. 291 299\n", + "Bandwidth Estimation 0.78 0.78\n", + "Bandwidth Bias 1.282 1.282\n", + "rho (h/b) 0.608 0.608\n", + "\n", + "Method Coef. S.E. t-stat P>|t| 95% CI \n", + "-------------------------------------------------------------------------\n", + "Conventional 2.407 0.634 3.795 1.475e-04 [1.164, 3.65]\n", + "Robust - - 3.122 1.796e-03 [0.869, 3.802]\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rdrobust_sharp_noadj = rdrobust(y=df['y'], x=df['score'], fuzzy=df['d'], c=0.0)\n", "rdrobust_sharp_noadj" @@ -165,9 +3249,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Call: rdrobust\n", + "Number of Observations: 1000\n", + "Polynomial Order Est. (p): 1\n", + "Polynomial Order Bias (q): 2\n", + "Kernel: Triangular\n", + "Bandwidth Selection: mserd\n", + "Var-Cov Estimator: NN\n", + "\n", + " Left Right\n", + "------------------------------------------------\n", + "Number of Observations 493 507\n", + "Number of Unique Obs. 493 507\n", + "Number of Effective Obs. 281 285\n", + "Bandwidth Estimation 0.732 0.732\n", + "Bandwidth Bias 1.22 1.22\n", + "rho (h/b) 0.6 0.6\n", + "\n", + "Method Coef. S.E. t-stat P>|t| 95% CI \n", + "-------------------------------------------------------------------------\n", + "Conventional 2.207 0.433 5.099 3.416e-07 [1.359, 3.056]\n", + "Robust - - 4.311 1.629e-05 [1.189, 3.172]\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rdrobust_sharp = rdrobust(y=df['y'], x=df['score'], fuzzy=df['d'], covs=df[cov_names], c=0.0)\n", "rdrobust_sharp" @@ -195,7 +3315,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -211,9 +3331,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method Coef. S.E. t-stat P>|t| 95% CI\n", + "-------------------------------------------------------------------------\n", + "Conventional 2.014 0.103 19.494 1.231e-84 [1.812, 2.217]\n", + "Robust - - 16.872 7.173e-64 [1.779, 2.247]\n", + "Design Type: Sharp\n", + "Cutoff: 0\n", + "First Stage Kernel: triangular\n", + "Final Bandwidth: [0.6607402]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", + "\n" + ] + } + ], "source": [ "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "\n", @@ -261,9 +3488,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.scatter(\n", " x=df['score'],\n", @@ -324,9 +6570,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Call: rdrobust\n", + "Number of Observations: 1000\n", + "Polynomial Order Est. (p): 1\n", + "Polynomial Order Bias (q): 2\n", + "Kernel: Triangular\n", + "Bandwidth Selection: mserd\n", + "Var-Cov Estimator: NN\n", + "\n", + " Left Right\n", + "------------------------------------------------\n", + "Number of Observations 493 507\n", + "Number of Unique Obs. 493 507\n", + "Number of Effective Obs. 213 224\n", + "Bandwidth Estimation 0.542 0.542\n", + "Bandwidth Bias 0.864 0.864\n", + "rho (h/b) 0.628 0.628\n", + "\n", + "Method Coef. S.E. t-stat P>|t| 95% CI \n", + "-------------------------------------------------------------------------\n", + "Conventional 4.64 3.052 1.52 1.285e-01 [-1.343, 10.622]\n", + "Robust - - 1.414 1.575e-01 [-1.952, 12.054]\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rdrobust_fuzzy_noadj = rdrobust(y=df['y'], x=df['score'], fuzzy=df['d'], c=0.0)\n", "rdrobust_fuzzy_noadj" @@ -347,9 +6629,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Call: rdrobust\n", + "Number of Observations: 1000\n", + "Polynomial Order Est. (p): 1\n", + "Polynomial Order Bias (q): 2\n", + "Kernel: Triangular\n", + "Bandwidth Selection: mserd\n", + "Var-Cov Estimator: NN\n", + "\n", + " Left Right\n", + "------------------------------------------------\n", + "Number of Observations 493 507\n", + "Number of Unique Obs. 493 507\n", + "Number of Effective Obs. 211 223\n", + "Bandwidth Estimation 0.54 0.54\n", + "Bandwidth Bias 0.88 0.88\n", + "rho (h/b) 0.614 0.614\n", + "\n", + "Method Coef. S.E. t-stat P>|t| 95% CI \n", + "-------------------------------------------------------------------------\n", + "Conventional 2.802 2.326 1.205 2.283e-01 [-1.757, 7.361]\n", + "Robust - - 1.097 2.728e-01 [-2.367, 8.381]\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rdrobust_fuzzy = rdrobust(y=df['y'], x=df['score'], fuzzy=df['d'], covs=df[cov_names], c=0.0)\n", "rdrobust_fuzzy" @@ -385,7 +6703,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -401,9 +6719,196 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but 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"output_type": "stream", + "text": [ + "Method Coef. S.E. t-stat P>|t| 95% CI\n", + "-------------------------------------------------------------------------\n", + "Conventional 0.979 0.726 1.348 1.777e-01 [-0.445, 2.403]\n", + "Robust - - 0.994 3.201e-01 [-0.831, 2.541]\n", + "Design Type: Fuzzy\n", + "Cutoff: 0\n", + "First Stage Kernel: triangular\n", + "Final Bandwidth: [0.61170069]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + "\n", + "/home/ubuntu/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py:2739: UserWarning:\n", + "\n", + "X does not have valid feature names, but LGBMClassifier was fitted with feature names\n", + "\n" + ] + } + ], "source": [ "ml_g = LGBMRegressor(n_estimators=500, learning_rate=0.01, verbose=-1)\n", "ml_m = LGBMClassifier(n_estimators=500, learning_rate=0.01, verbose=-1)\n", @@ -442,7 +6947,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +6960,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -493,9 +6998,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method Coef. S.E. t-stat P>|t| 95% CI\n", + "-------------------------------------------------------------------------\n", + "Conventional 1.876 0.101 18.619 2.241e-77 [1.678, 2.073]\n", + "Robust - - 15.992 1.443e-57 [1.612, 2.062]\n", + "Design Type: Sharp\n", + "Cutoff: 0\n", + "First Stage Kernel: triangular\n", + "Final Bandwidth: [0.61771229]\n" + ] + } + ], "source": [ "rdflex_sharp_stack = RDFlex(dml_data_sharp,\n", " ml_g,\n", @@ -516,9 +7036,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method Coef. S.E. t-stat P>|t| 95% CI\n", + "-------------------------------------------------------------------------\n", + "Conventional 1.726 0.609 2.835 4.577e-03 [0.533, 2.918]\n", + "Robust - - 2.642 8.243e-03 [0.483, 3.261]\n", + "Design Type: Fuzzy\n", + "Cutoff: 0\n", + "First Stage Kernel: triangular\n", + "Final Bandwidth: [0.54550506]\n" + ] + } + ], "source": [ "rdflex_fuzzy_stack = RDFlex(dml_data_fuzzy,\n", " ml_g,\n", @@ -540,7 +7075,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -556,9 +7091,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n", From 8ad615e9630e99e1d73e66207649b722fbe7308b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 21:31:05 +0000 Subject: [PATCH 06/16] update formatting in example --- doc/examples/py_double_ml_plpr.ipynb | 96 +++++++++++++++++----------- 1 file changed, 57 insertions(+), 39 deletions(-) diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb index 9db17da7..d56ae6d2 100644 --- a/doc/examples/py_double_ml_plpr.ipynb +++ b/doc/examples/py_double_ml_plpr.ipynb @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate treatment effects for static panel models with fixed effects in a partially linear panel regression [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) model. The model is based on [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." + "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate treatment effects for static panel models with fixed effects in a partially linear panel regression [DoubleMLPLPR](https://docs.doubleml.org/stable/guide/models.html#partially-linear-models-plm) model. The model is based on [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." ] }, { @@ -50,9 +50,9 @@ "source": [ "## Data\n", "\n", - "We will use the implemented data generating process [make_plpr_CP2025](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plpr_CP2025.html) to generate data similar to the simulation in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011). For exposition, we use the simple linear `dgp_type=\"dgp1\"`, with 150 units, 10 time periods per unit, and a true treatment effect of `theta=0.5`.\n", + "We will use the implemented data generating process [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators) to generate data similar to the simulation in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011). For exposition, we use the simple linear `dgp_type=\"dgp1\"`, with 150 units, 10 time periods per unit, and a true treatment effect of `theta=0.5`.\n", "\n", - "We set `time_type=\"int\"` such that the time variable values will be integers. It's also possible to use `\"float\"` or `\"datetime\"` time variables with [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr)." + "We set `time_type=\"int\"` such that the time variable values will be integers. It's also possible to use `\"float\"` or `\"datetime\"` time variables with [DoubleMLPLPR](https://docs.doubleml.org/stable/api/dml_models.html#doubleml-plm)." ] }, { @@ -297,10 +297,12 @@ "\n", "The PLPR model takes the form\n", "\n", + "$$\n", "\\begin{align*}\n", " Y_{it} &= \\theta_0 D_{it} + g_1(X_{it}) + \\alpha_i^* + U_{it}, \\\\\n", " D_{it} &= m_1(X_{it}) + \\gamma_i + V_{it},\n", "\\end{align*}\n", + "$$\n", "\n", "where\n", "- $Y_{it}$ outcome, $D_{it}$ treatment, $X_{it}$ covariates, $\\theta_0$ causal treatment effect\n", @@ -312,10 +314,12 @@ "\n", "Alternatively we can write the partialling-out PLPR as \n", "\n", + "$$\n", "\\begin{align*}\n", " Y_{it} &= \\theta_0 V_{it} + \\ell_1(X_{it}) + \\alpha_i + U_{it}, \\\\\n", " V_{it} &= D_{it} - m_1(X_{it}) - \\gamma_i,\n", "\\end{align*}\n", + "$$\n", "\n", "with nuisance function $\\ell_1$ and fixed effect $\\alpha_i$." ] @@ -324,23 +328,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Assumptions\n", + "### Assumptions\n", "\n", "Define $\\xi_i$ as time-invariant heterogeneity terms influencing outcome and treatment and $L_{t-1}(W_i) = \\{ W_{i1}, \\dots, W_{it-1} \\}$ as lags of a random variable $W_{it}$ at wave $t$.\n", "\n", - "- No feedback to predictors\n", + "- *No feedback to predictors*\n", "$$ X_{it} \\perp L_{t-1} (Y_i, D_i) \\mid L_{t-1} (X_i), \\xi_i $$\n", - "- Static panel\n", + "- *Static panel*\n", "$$ Y_{it}, D_{it} \\perp L_{t-1} (Y_i, X_i, D_i) \\mid X_{it}, \\xi_i $$\n", - "- Selection on observables and omitted time-invariant variables\n", + "- *Selection on observables and omitted time-invariant variables*\n", "$$ Y_{it} (.) \\perp D_{it} \\mid X_{it}, \\xi_i $$\n", - "- Homogeneity and linearity of the treatment effect\n", + "- *Homogeneity and linearity of the treatment effect*\n", "$$ \\mathbb{E} [Y_{it}(d) - Y_{it}(0) \\mid X_{it}, \\xi_i] = d \\theta_0 $$\n", - "- Additive Separability\n", + "- *Additive Separability*\n", + "$$\n", "\\begin{align*}\n", "\\mathbb{E} [Y_{it}(0) \\mid X_{it}, \\xi_i] &= g_1(X_{it}) + \\alpha^*_i \\quad \\text{where } \\alpha^*_i = \\alpha^*(\\xi_i), \\\\\n", "\\mathbb{E} [D_{it} \\mid X_{it}, \\xi_i] &= m_1(X_{it}) + \\gamma_i \\quad \\text{where } \\gamma_i = \\gamma(\\xi_i)\n", - "\\end{align*} \n", + "\\end{align*}\n", + "$$\n", "\n", "For more information, see [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011)." ] @@ -349,9 +355,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To estimate the causal effect, we can create a [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object. \n", - "\n", - "The model described in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) uses block-k-fold cross-fitting, where the entire time series of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit which is common with panel data. Furthermore, cluster robust standard error are employed. [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) implements both aspects by using `id_col` as the cluster variable.\n", + "To estimate the causal effect, we can create a [DoubleMLPLPR](https://docs.doubleml.org/stable/api/dml_models.html#doubleml-plm) object. The model described in [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) uses block-k-fold cross-fitting, where the entire time series of the sampled unit is allocated to one fold to allow for possible serial correlation within each unit which is common with panel data. Furthermore, cluster robust standard error are employed. [DoubleMLPLPR](https://docs.doubleml.org/stable/guide/models.html#partially-linear-models-plm) implements both aspects by using `id_col` as the cluster variable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimation Approaches\n", "\n", "[Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) describes multiple estimation approaches, which can be set with the `approach` parameter. Depending on the type of `approach`, different data transformations are performed along the way." ] @@ -360,16 +371,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Approaches\n", + "### Correlated Random Effects\n", "\n", - "#### Correlated Random Effect\n", + "The correlated random effects (cre) approaches includes the a general approach (`cre_general`) and an approach relying on normality assumptions (`cre_normal`).\n", "\n", - "`cre_general` approach:\n", + "#### `cre_general`\n", "\n", - "- Learning $\\ell_1$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$,\n", - "- First learning $\\tilde{m}_1$ from $\\{ D_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$, with predictions $\\hat{m}_{1,it} = \\tilde{m}_1 (X_{it}, \\bar{X}_i) $\n", - " - Calculate $\\hat{\\bar{m}}_i = T^{-1} \\sum_{t=1}^T \\hat{m}_{1,it} $,\n", - " - Calculate final nuisance part as $ \\hat{m}^*_1 (X_{it}, \\bar{X}_i, \\bar{D}_i) = \\hat{m}_{1,it} + \\bar{D}_i - \\hat{\\bar{m}}_i $. " + "The `cre_general` approach:\n", + "\n", + "- Learning $\\ell_1$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$.\n", + "- First learning $\\tilde{m}_1$ from $\\{ D_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$, with predictions $\\hat{m}_{1,it} = \\tilde{m}_1 (X_{it}, \\bar{X}_i)$\n", + " - Calculate $\\hat{\\bar{m}}_i = T^{-1} \\sum_{t=1}^T \\hat{m}_{1,it}$,\n", + " - Calculate final nuisance part as $\\hat{m}^*_1 (X_{it}, \\bar{X}_i, \\bar{D}_i) = \\hat{m}_{1,it} + \\bar{D}_i - \\hat{\\bar{m}}_i$." ] }, { @@ -389,7 +402,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can look at the the transformed data using the `data_transform` property after the [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object was created." + "We can look at the the transformed data using the `data_transform` property after the `DoubleMLPLPR` object was created." ] }, { @@ -612,9 +625,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After fitting the model, we can print the [DoubleMLPLPR](https://docs.doubleml.org/stable/api/generated/doubleml.plm.DoubleMLPLPR.html) object.\n", - "\n", - "The Data Summary corresponds to the transformed data. Additional Information at the end also includes a Pre-Transformation Data Summary." + "After fitting the model, we can print the `DoubleMLPLPR` object. The data summary corresponds to the transformed data. Additional Information at the end also includes a pre-transformation data summary." ] }, { @@ -681,9 +692,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`cre_normal` approach:\n", + "#### `cre_normal`\n", + "\n", + "The `cre_normal` approach:\n", "\n", - "Under the assumption that the conditional distribution $ D_{i1}, \\dots, D_{iT} \\mid X_{i1}, \\dots X_{iT} $ is multivariate normal (see [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) for further details):\n", + "Under the assumption that the conditional distribution $D_{i1}, \\dots, D_{iT} \\mid X_{i1}, \\dots X_{iT}$ is multivariate normal (see [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) for further details):\n", "- Learn $\\ell_1$ from $\\{ Y_{it}, X_{it}, \\bar{X}_i : t=1,\\dots, T \\}_{i=1}^N$,\n", "- Learn $m^*_{1}$ from $\\{ D_{it}, X_{it}, \\bar{X}_i, \\bar{D}_i: t=1,\\dots, T \\}_{i=1}^N$." ] @@ -745,14 +758,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Transformation Approaches\n", + "### Transformation Approaches\n", "\n", - "`fd_exact` approach:\n", + "The transformation approaches include first differences (`fd_exact`) and within group (`wg_approx`) transformations.\n", "\n", - "Consider FD transformation $Q(Y_{it})= Y_{it} - Y_{it-1} $, under the assumptions from above, [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) show that $\\mathbb{E}[Y_{it}-Y_{it-1} | X_{it-1},X_{it}] =\\Delta \\ell_1 (X_{it-1}, X_{it})$ and $\\mathbb{E}[D_{it}-D_{it-1} | X_{it-1},X_{it}] =\\Delta m_1 (X_{it-1}, X_{it})$. Therefore, the transformed nuisance function can be learnt as\n", + "#### `fd_exact`\n", "\n", - "- $ \\Delta \\ell_1 (X_{it-1}, X_{it}) $ from $ \\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $,\n", - "- $ \\Delta m_1 (X_{it-1}, X_{it}) $ from $ \\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N $.\n" + "Consider first differences (FD) transformation (`fd_exact`) $Q(Y_{it})= Y_{it} - Y_{it-1}$, under the assumptions from above, [Clarke and Polselli (2025)](https://doi.org/10.1093/ectj/utaf011) show that $\\mathbb{E}[Y_{it}-Y_{it-1} | X_{it-1},X_{it}] =\\Delta \\ell_1 (X_{it-1}, X_{it})$ and $\\mathbb{E}[D_{it}-D_{it-1} | X_{it-1},X_{it}] =\\Delta m_1 (X_{it-1}, X_{it})$. Therefore, the transformed nuisance function can be learnt as\n", + "\n", + "- $\\Delta \\ell_1 (X_{it-1}, X_{it})$ from $\\{ Y_{it}-Y_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N$,\n", + "- $\\Delta m_1 (X_{it-1}, X_{it})$ from $\\{ D_{it}-D_{it-1}, X_{it-1}, X_{it} : t=2, \\dots , T \\}_{i=1}^N$." ] }, { @@ -995,13 +1010,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`wg_approx` approach:\n", + "#### `wg_approx`\n", + "\n", + "For within-group (WG) transformation (`wg_approx`) $Q(X_{it})= X_{it} - \\bar{X}_{i}$, where $\\bar{X}_{i} = T^{-1} \\sum_{t=1}^T X_{it}$, approximate the model as\n", "\n", - "For WG transformation $Q(X_{it})= X_{it} - \\bar{X}_{i} $, where $ \\bar{X}_{i} = T^{-1} \\sum_{t=1}^T X_{it} $. Approximate the model as\n", + "$$\n", "\\begin{align*}\n", " Q(Y_{it}) &\\approx \\theta_0 Q(D_{it}) + g_1 (Q(X_{it})) + Q(U_{it}), \\\\\n", " Q(D_{it}) &\\approx m_1 (Q(X_{it})) + Q(V_{it}).\n", "\\end{align*}\n", + "$$\n", "\n", "Similarly for the partialling-out PLPR\n", "\n", @@ -1009,8 +1027,8 @@ "Q(Y_{it}) \\approx \\theta_0 Q(V_{it}) + \\ell_1 (Q(X_{it})) + Q(U_{it}).\n", "$$\n", "\n", - "- $\\ell_1$ can be learnt from transformed data $ \\{ Q(Y_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $,\n", - "- $m_1$ can be learnt from transformed data $ \\{ Q(D_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N $." + "- $\\ell_1$ can be learnt from transformed data $\\{ Q(Y_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N$,\n", + "- $m_1$ can be learnt from transformed data $\\{ Q(D_{it}), Q(X_{it}) : t=1,\\dots,T \\}_{i=1}^N$." ] }, { @@ -1476,7 +1494,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Feature preprocessing pipelines\n", + "## Feature preprocessing pipelines\n", "\n", "We can incorporate preprocessing pipelines. For example, when using Lasso, we may want to include polynomial and interaction terms. Here, we create a class that allows us to include, for example, polynomials of order 3 and interactions between all variables." ] @@ -1546,7 +1564,7 @@ "source": [ "We can apply the polynomial and intercation transformation for specific sets of covariates. For example, for the `fd_exact` approach, we can apply it to the original $X_{it}$ and lags $X_{it-1}$ seperately using `ColumnTransformer`.\n", "\n", - "To achieve this, we pass need to pass the corresponding indices for these two sets. [DoubleMLPLPR](http://docs.doubleml.org/stable/guide/models.html#partially-linear-panel-regression-model-plpr) stacks sets $X_{it}$ and $X_{it-1}$ column-wise. Given our example data has 30 covariates, this means that the first 30 features in the nuisance estimation correspond to the original $X_{it}$, and the last 30 correspond to lags $X_{it-1}$. Therefore we define the indices `indices_x` and `indices_x_tr` as below." + "To achieve this, we pass need to pass the corresponding indices for these two sets. `DoubleMLPLPR` stacks sets $X_{it}$ and $X_{it-1}$ column-wise. Given our example data has 30 covariates, this means that the first 30 features in the nuisance estimation correspond to the original $X_{it}$, and the last 30 correspond to lags $X_{it-1}$. Therefore we define the indices `indices_x` and `indices_x_tr` as below." ] }, { @@ -1593,7 +1611,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3495,7 +3513,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Hyperparameter tuning\n", + "## Hyperparameter tuning\n", "\n", "In this section we will use the `tune_ml_models()` method to tune hyperparameters using the [Optuna](https://optuna.org/) package. More details can found in the [Python: Hyperparametertuning with Optuna](https://docs.doubleml.org/stable/examples/learners/py_optuna.html) example notebook.\n", "\n", From a43f681e19d9055e884b834f256a8e4a00f54e15 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 21:31:28 +0000 Subject: [PATCH 07/16] fix: update parameter names in examples --- doc/guide/data/panel_data.rst | 4 ++-- doc/guide/models/plm/plm_models.inc | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/guide/data/panel_data.rst b/doc/guide/data/panel_data.rst index 0b6f7e26..e1125570 100644 --- a/doc/guide/data/panel_data.rst +++ b/doc/guide/data/panel_data.rst @@ -54,13 +54,13 @@ Example usage from doubleml.plm.datasets import make_plpr_CP2025 np.random.seed(42) - df = make_plpr_CP2025(num_id=100, num_t=5, x_dim=5) + df = make_plpr_CP2025(num_id=100, num_t=5, dim_x=5) dml_data = dml.data.DoubleMLPanelData( df, y_col="y", d_cols="d", id_col="id", - t_col="t", + t_col="time", x_cols=["x1", "x2", "x3", "x4", "x5"], static_panel=True ) diff --git a/doc/guide/models/plm/plm_models.inc b/doc/guide/models/plm/plm_models.inc index 824ab840..853b29cf 100644 --- a/doc/guide/models/plm/plm_models.inc +++ b/doc/guide/models/plm/plm_models.inc @@ -125,8 +125,8 @@ Partially linear panel regression model (PLPR) ml_l = clone(learner) ml_m = clone(learner) data = make_plpr_CP2025(num_id=250, num_t=10, dim_x=30, theta=0.5, dgp_type='dgp1') - obj_dml_data = DoubleMLPanelData(data, 'y', 'd', 'time', 'id', static_panel=True) - dml_plpr_obj = DoubleMLPLPR(obj_dml_data, ml_l, ml_m) + obj_dml_data = dml.data.DoubleMLPanelData(data, 'y', 'd', 'time', 'id', static_panel=True) + dml_plpr_obj = dml.DoubleMLPLPR(obj_dml_data, ml_l, ml_m) dml_plpr_obj.fit() print(dml_plpr_obj) From 00fc574f600b47b42a765de5bcd80fdafa48af23 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 21:45:21 +0000 Subject: [PATCH 08/16] fix format issues in plpr model description --- doc/guide/models/plm/plpr.rst | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/doc/guide/models/plm/plpr.rst b/doc/guide/models/plm/plpr.rst index 660b72ee..7049c086 100644 --- a/doc/guide/models/plm/plpr.rst +++ b/doc/guide/models/plm/plpr.rst @@ -57,9 +57,11 @@ Assumptions `(Clarke and Polselli, 2025) ` :math:`\mathbb{E} [Y_{it}(d) - Y_{it}(0) | X_{it}, \xi_i] = d \theta_0` - **Additive Separability** - :math:`\mathbb{E} [Y_{it}(0) | X_{it}, \xi_i] &= g_1(X_{it}) + \alpha^*_i, & &\alpha^*_i = \alpha^*(\xi_i)`, + .. math:: + + &\mathbb{E} [Y_{it}(0) | X_{it}, \xi_i] = g_1(X_{it}) + \alpha^*_i, & &\alpha^*_i = \alpha^*(\xi_i), - :math:`\mathbb{E} [D_{it} | X_{it}, \xi_i] &= m_1(X_{it}) + \gamma_i, & &\gamma_i = \gamma(\xi_i)` + &\mathbb{E} [D_{it} | X_{it}, \xi_i] = m_1(X_{it}) + \gamma_i, & &\gamma_i = \gamma(\xi_i) **Correlated Random Effect (CRE) Approaches** @@ -130,9 +132,9 @@ two are transformation approaches. This can be selected with the ``approach`` pa - First learn :math:`\tilde{m}_1(X_{it}, \bar{X}_i)` from :math:`\{ D_{it}, X_{it}, \bar{X}_i : t=1,\dots, T \}_{i=1}^N`, with predictions :math:`\hat{m}_{1,it} = \tilde{m}_1 (X_{it}, \bar{X}_i)` - - Calculate :math:`\hat{\bar{m}}_i = T^{-1} \sum_{t=1}^T \hat{m}_{1,it}`, + - Calculate :math:`\hat{\bar{m}}_i = T^{-1} \sum_{t=1}^T \hat{m}_{1,it}`, - - Calculate final nuisance part as :math:`\hat{m}^*_1 (X_{it}, \bar{X}_i, \bar{D}_i) = \hat{m}_{1,it} + \bar{D}_i - \hat{\bar{m}}_i`, + - Calculate final nuisance part as :math:`\hat{m}^*_1 (X_{it}, \bar{X}_i, \bar{D}_i) = \hat{m}_{1,it} + \bar{D}_i - \hat{\bar{m}}_i`, where :math:`\hat{m}^*_1 (X_{it}, \bar{X}_i, \bar{D}_i) = \mathbb{E}[D_{it} | X_{it}, \bar{X}_i] + c_i`. @@ -174,7 +176,7 @@ Approximating the model gives Similarly, .. math:: - Q(Y_{it}) &\approx \theta_0 Q(V_{it}) + \ell_1 (Q(X_{it})) + Q(U_{it}). + Q(Y_{it}) \approx \theta_0 Q(V_{it}) + \ell_1 (Q(X_{it})) + Q(U_{it}). - Learn :math:`\ell_1` from transformed data :math:`\{ Q(Y_{it}), Q(X_{it}) : t=1,\dots,T \}_{i=1}^N`, From 14c6c321a4f69e8b3407c7f54c59b55c82f20ecd Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 21:45:37 +0000 Subject: [PATCH 09/16] add link exception --- doc/conf.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index bde22ea4..618306f4 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -277,7 +277,9 @@ # Valid DOI; Causes 403 Client Error: Forbidden for url:... "https://doi.org/10.1093/ectj/utab019", # Valid DOI; Causes 403 Client Error: Forbidden for url:... - "https://doi.org/10.1093/ectj/utaf011" + "https://doi.org/10.1093/ectj/utaf011", + # Valid DOI; Causes 403 Client Error: Forbidden for url:... + "https://doi.org/10.2307/1913646" ] # To execute R code via jupyter-execute one needs to install the R kernel for jupyter From 78136842834f35b06510a0e01607994499e34a19 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 14 Jan 2026 21:45:51 +0000 Subject: [PATCH 10/16] clean nb to enable html rendering of pipeline --- doc/examples/py_double_ml_plpr.ipynb | 3219 +------------------------- 1 file changed, 65 insertions(+), 3154 deletions(-) diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb index d56ae6d2..16ea64ed 100644 --- a/doc/examples/py_double_ml_plpr.ipynb +++ b/doc/examples/py_double_ml_plpr.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -59,207 +59,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"Instrument variable(s): None\n", - "Time variable: time\n", - "Id variable: id\n", - "Static panel data: True\n", - "No. Unique Ids: 150\n", - "No. Observations: 1500\n", - "\n", - "------------------ Score & Algorithm ------------------\n", - "Score function: partialling out\n", - "Static panel model approach: cre_general\n", - "\n", - "------------------ Machine Learner ------------------\n", - "Learner ml_l: LassoCV()\n", - "Learner ml_m: LassoCV()\n", - "Out-of-sample Performance:\n", - "Regression:\n", - "Learner ml_l RMSE: [[1.8336022]]\n", - "Learner ml_m RMSE: [[0.99534683]]\n", - "\n", - "------------------ Resampling ------------------\n", - "No. folds per cluster: 5\n", - "No. folds: 5\n", - "No. repeated sample splits: 1\n", - "\n", - "------------------ Fit Summary ------------------\n", - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 0.490173 0.026161 18.737077 2.468215e-78 0.438899 0.541447\n", - "\n", - "------------------ Additional Information -------------\n", - "Cluster variable(s): ['id']\n", - "\n", - "Pre-Transformation Data Summary: \n", - "Outcome variable: y\n", - "Treatment variable(s): ['d']\n", - "Covariates: ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30']\n", - "No. Observations: 1500\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_plpr_cre_general.fit()\n", "print(dml_plpr_cre_general)" @@ -705,16 +255,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d 0.505807 0.027168 18.617927 2.299468e-77 0.45256 0.559055\n" - ] - } - ], + "outputs": [], "source": [ "dml_plpr_cre_normal = DoubleMLPLPR(data_obj, ml_l=ml_l, ml_m=ml_m, approach=\"cre_normal\", n_folds=5)\n", "dml_plpr_cre_normal.fit()\n", @@ -730,26 +271,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.94278854],\n", - " [-0.94278854],\n", - " [-0.94278854],\n", - " ...,\n", - " [ 0.15320478],\n", - " [ 0.15320478],\n", - " [ 0.15320478]], shape=(1500, 1))" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dml_plpr_cre_normal.d_mean" ] @@ -760,7 +284,7 @@ "source": [ "### Transformation Approaches\n", "\n", - "The transformation approaches include first differences (`fd_exact`) and within group (`wg_approx`) transformations.\n", + "The transformation approaches include first differences (`fd_exact`) and within-group (`wg_approx`) transformations.\n", "\n", "#### `fd_exact`\n", "\n", @@ -774,207 +298,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " coef std err t P>|t| 2.5 % 97.5 %\n", - "d_demean 1.137408 0.004974 228.656233 0.0 1.127659 1.147158" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plpr_tune_wg = DoubleMLPLPR(dml_data_tune, clone(ml_boost), clone(ml_boost), approach=\"wg_approx\", n_folds=5)\n", "\n", @@ -3884,33 +819,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True treatment effect: 0.5\n", - "\n", - " Model theta se ci_lower ci_upper\n", - "cre_general 0.507695 0.008154 0.491713 0.523677\n", - " cre_normal 0.472977 0.010134 0.453114 0.492839\n", - " fd_exact 0.551595 0.008651 0.534638 0.568551\n", - " wg_approx 1.137408 0.004974 1.127659 1.147158\n" - ] - }, - { - "data": { - "image/png": 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"execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================== DoubleMLPLPR Object ==================\n", + "\n", + "------------------ Data Summary ------------------\n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x1_mean', 'x2_mean', 'x3_mean', 'x4_mean', 'x5_mean', 'x6_mean', 'x7_mean', 'x8_mean', 'x9_mean', 'x10_mean', 'x11_mean', 'x12_mean', 'x13_mean', 'x14_mean', 'x15_mean', 'x16_mean', 'x17_mean', 'x18_mean', 'x19_mean', 'x20_mean', 'x21_mean', 'x22_mean', 'x23_mean', 'x24_mean', 'x25_mean', 'x26_mean', 'x27_mean', 'x28_mean', 'x29_mean', 'x30_mean']\n", + "Instrument variable(s): None\n", + "Time variable: time\n", + "Id variable: id\n", + "Static panel data: True\n", + "No. Unique Ids: 150\n", + "No. Observations: 1500\n", + "\n", + "------------------ Score & Algorithm ------------------\n", + "Score function: partialling out\n", + "Static panel model approach: cre_general\n", + "\n", + "------------------ Machine Learner ------------------\n", + "Learner ml_l: LassoCV()\n", + "Learner ml_m: LassoCV()\n", + "Out-of-sample Performance:\n", + "Regression:\n", + "Learner ml_l RMSE: [[1.8336022]]\n", + "Learner ml_m RMSE: [[0.99534683]]\n", + "\n", + "------------------ Resampling ------------------\n", + "No. folds per cluster: 5\n", + "No. folds: 5\n", + "No. repeated sample splits: 1\n", + "\n", + "------------------ Fit Summary ------------------\n", + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.490173 0.026161 18.737077 2.468215e-78 0.438899 0.541447\n", + "\n", + "------------------ Additional Information -------------\n", + "Cluster variable(s): ['id']\n", + "\n", + "Pre-Transformation Data Summary: \n", + "Outcome variable: y\n", + "Treatment variable(s): ['d']\n", + "Covariates: ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30']\n", + "No. Observations: 1500\n", + "\n" + ] + } + ], "source": [ "dml_plpr_cre_general.fit()\n", "print(dml_plpr_cre_general)" @@ -253,9 +703,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.505807 0.027168 18.617927 2.299468e-77 0.45256 0.559055\n" + ] + } + ], "source": [ "dml_plpr_cre_normal = DoubleMLPLPR(data_obj, ml_l=ml_l, ml_m=ml_m, approach=\"cre_normal\", n_folds=5)\n", "dml_plpr_cre_normal.fit()\n", @@ -271,9 +730,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.94278854],\n", + " [-0.94278854],\n", + " [-0.94278854],\n", + " ...,\n", + " [ 0.15320478],\n", + " [ 0.15320478],\n", + " [ 0.15320478]], shape=(1500, 1))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dml_plpr_cre_normal.d_mean" ] @@ -296,9 +772,209 @@ }, { "cell_type": "code", - 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Pipeline(steps=[('columntransformer',\n",
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" + ], + "text/plain": [ + "Pipeline(steps=[('columntransformer',\n", + " ColumnTransformer(remainder='passthrough',\n", + " transformers=[('poly_x', PolyPlus(),\n", + " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n", + " 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23,\n", + " 24, 25, 26, 27, 28, 29]),\n", + " ('poly_x_tr', PolyPlus(),\n", + " [30, 31, 32, 33, 34, 35, 36,\n", + " 37, 38, 39, 40, 41, 42, 43,\n", + " 44, 45, 46, 47, 48, 49, 50,\n", + " 51, 52, 53, 54, 55, 56, 57,\n", + " 58, 59])])),\n", + " ('standardscaler', StandardScaler()),\n", + " ('lassocv', LassoCV(cv=2, n_alphas=20, n_jobs=5))])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ml_lasso = make_pipeline(\n", - " preprocessor, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + " preprocessor, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n", ")\n", "\n", "ml_lasso" @@ -530,9 +2103,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_diff 0.516443 0.018066 28.585995 1.003247e-179 0.481034 0.551852\n" + ] + } + ], "source": [ "plpr_lasso_fd = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso), clone(ml_lasso), approach=\"fd_exact\", n_folds=5)\n", "plpr_lasso_fd.fit(store_models=True)\n", @@ -548,9 +2130,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1050" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_lasso_fd.models[\"ml_m\"][\"d_diff\"][0][0].named_steps[\"lassocv\"].n_features_in_" ] @@ -564,9 +2157,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.569761 0.028406 20.057831 1.724430e-89 0.514086 0.625436\n" + ] + }, + { + "data": { + "text/plain": [ + "1051" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_lasso_cre_normal = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso), clone(ml_lasso), approach=\"cre_normal\", n_folds=5)\n", "plpr_lasso_cre_normal.fit(store_models=True)\n", @@ -583,7 +2195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -599,15 +2211,34 @@ ")\n", "\n", "ml_lasso_wg = make_pipeline(\n", - " preprocessor_wg, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + " preprocessor_wg, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n", ")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_demean 1.152402 0.014064 81.940739 0.0 1.124837 1.179966\n" + ] + }, + { + "data": { + "text/plain": [ + "525" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_lasso_wg = DoubleMLPLPR(dml_data_dgp3, clone(ml_lasso_wg), clone(ml_lasso_wg), approach=\"wg_approx\", n_folds=5)\n", "plpr_lasso_wg.fit(store_models=True)\n", @@ -631,9 +2262,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "39" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x_cols = dml_data_dgp3.x_cols \n", "x_cols_to_pre = [\"x3\", \"x6\", \"x22\"]\n", @@ -651,7 +2293,7 @@ " remainder=\"passthrough\",\n", ")\n", "ml_lasso_alt = make_pipeline(\n", - " preprocessor_alt, StandardScaler(), LassoCV(alphas=20, cv=2, n_jobs=5)\n", + " preprocessor_alt, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n", ")\n", "\n", "plpr_lasso_wg.learner[\"ml_l\"] = ml_lasso_alt\n", @@ -663,9 +2305,441 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ColumnTransformer(remainder='passthrough',\n",
+       "                  transformers=[('poly_x', PolyPlus(), [2, 5, 21])])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "ColumnTransformer(remainder='passthrough',\n", + " transformers=[('poly_x', PolyPlus(), [2, 5, 21])])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps['columntransformer']" ] @@ -681,9 +2755,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['poly_x__x2', 'poly_x__x5', 'poly_x__x21', 'poly_x__x2^2',\n", + " 'poly_x__x2 x5', 'poly_x__x2 x21', 'poly_x__x5^2',\n", + " 'poly_x__x5 x21', 'poly_x__x21^2', 'poly_x__x2^3', 'poly_x__x5^3',\n", + " 'poly_x__x21^3', 'remainder__x0', 'remainder__x1', 'remainder__x3',\n", + " 'remainder__x4', 'remainder__x6', 'remainder__x7', 'remainder__x8',\n", + " 'remainder__x9', 'remainder__x10', 'remainder__x11',\n", + " 'remainder__x12', 'remainder__x13', 'remainder__x14',\n", + " 'remainder__x15', 'remainder__x16', 'remainder__x17',\n", + " 'remainder__x18', 'remainder__x19', 'remainder__x20',\n", + " 'remainder__x22', 'remainder__x23', 'remainder__x24',\n", + " 'remainder__x25', 'remainder__x26', 'remainder__x27',\n", + " 'remainder__x28', 'remainder__x29'], dtype=object)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_lasso_wg.models[\"ml_l\"][\"d_demean\"][0][0].named_steps['columntransformer'].get_feature_names_out()\n" ] @@ -701,7 +2797,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -716,7 +2812,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -744,9 +2840,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb8bee93245c430fb5fce83c2c66eaf2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefstd errtP>|t|2.5 %97.5 %
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\n", + "
" + ], + "text/plain": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.50256 0.008025 62.620517 0.0 0.48683 0.518289" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_tune_cre_general = DoubleMLPLPR(dml_data_tune, clone(ml_boost), clone(ml_boost), approach=\"cre_general\", n_folds=5)\n", "\n", @@ -768,9 +2945,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "508dec2d1acd4dfe81fc4d2758acaaef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefstd errtP>|t|2.5 %97.5 %
d0.4617590.01030844.7968540.00.4415560.481962
\n", + "" + ], + "text/plain": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d 0.461759 0.010308 44.796854 0.0 0.441556 0.481962" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_tune_cre_normal = DoubleMLPLPR(dml_data_tune, clone(ml_boost), clone(ml_boost), approach=\"cre_normal\", n_folds=5)\n", "\n", @@ -785,9 +3043,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1acbbcfc44ee4b9b8c995f6e6620f788", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefstd errtP>|t|2.5 %97.5 %
d_diff0.5456980.00892861.123730.00.52820.563196
\n", + "" + ], + "text/plain": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_diff 0.545698 0.008928 61.12373 0.0 0.5282 0.563196" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_tune_fd = DoubleMLPLPR(dml_data_tune, clone(ml_boost), clone(ml_boost), approach=\"fd_exact\", n_folds=5)\n", "\n", @@ -802,9 +3141,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "73577cb5b19b428dab75d207746df8db", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefstd errtP>|t|2.5 %97.5 %
d_demean1.13650.005177219.5478460.01.1263541.146646
\n", + "" + ], + "text/plain": [ + " coef std err t P>|t| 2.5 % 97.5 %\n", + "d_demean 1.1365 0.005177 219.547846 0.0 1.126354 1.146646" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plpr_tune_wg = DoubleMLPLPR(dml_data_tune, clone(ml_boost), clone(ml_boost), approach=\"wg_approx\", n_folds=5)\n", "\n", @@ -819,9 +3239,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True treatment effect: 0.5\n", + "\n", + " Model theta se ci_lower ci_upper\n", + "cre_general 0.502560 0.008025 0.486830 0.518289\n", + " cre_normal 0.461759 0.010308 0.441556 0.481962\n", + " fd_exact 0.545698 0.008928 0.528200 0.563196\n", + " wg_approx 1.136500 0.005177 1.126354 1.146646\n" + ] + }, + { + "data": { + "image/png": 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lRYsWLVLTpk3t+o0bN05BQUGqXLmyWrdurb1792rq1Km29aiuxWKx2M6WzM7O1rlz52yXiOb1fe7Tp4/d/NW9e3dVqVLFNlflKMj72bdvX7vvXEG+65ePS0tL09mzZ3XHHXdIkq1+q9WqL7/8Ul26dMn12km5v69PPPGEXVvr1q2VnZ2tY8eOSbp0dlZ8fLx69epl+8ydPXtWLi4uatasmX744Qdbbe7u7lq/fn2h1ykDAJQthFIAgOuar6+vpP/9Az8/x44dk9lsVs2aNe3aK1euLH9/f9s/knJcHjxJkp+fnySpatWqebZf+Q8ls9msGjVq2LXdfPPNkuzXxFmxYoXuuOMOeXh4KCAgQEFBQXr33XeVkJCQ6xgiIiLyO0xJl9ba2r17t6pWrarbb79dr7zyil2AlHOstWvXzjX2lltuyfVaeHh4KCgoyK6tYsWKDv3jcM+ePbr//vvl5+cnX19fBQUF2RaSzjnGnEup6tWrl+/2XF1dddNNN9m1HTt2TKGhobkCyjp16tiel6SePXuqZcuWGjBggEJCQvTQQw9p2bJldgHVCy+8IB8fH91+++2qVauWBg8ebHd5X1EkJydL+l+QeuzYsTzfgyvrLqjQ0NBci7/n9dm73MGDB2UYhsaMGWMLg3J+xo0bJ0mKjY3NNc7NzU0PPvigPvnkE/3000/6+++/i3SZYw4PDw+tWbNGa9assW1348aNub5Tjho7dqzWrFmjdevWadeuXTp58qQeffTRXP2eeOIJrVmzRl9//bWGDx+uixcvOrR2mnQpsJk+fbpq1aoli8WiSpUqKSgoSLt27crz+1yrVi27xyaTSTVr1sz1HhXk/bxyjijId/38+fN69tlnFRISIk9PTwUFBdm2l1N/XFycEhMTHfquSrnn0ZzLIHPmjr/++kvSpbD/ys/dd999Z/vMWSwWvf766/r2228VEhKiO++8U2+88YZOnz7tUB0AgLKHNaUAANc1X19fhYaGavfu3QUal9eZN3m52iLNV2s3rlhQ2xE///yz/vGPf+jOO+/UO++8oypVqsjNzU0LFy7Mc2Hty89kuJYePXqodevW+ve//63vvvtOb775pl5//XV98cUX6tixY4HrLOyC1fHx8WrTpo18fX01YcIERUZGysPDQ7///rteeOGFq56tdC2Xn41SUJ6envrpp5/0ww8/aOXKlVq1apWWLl2qu+++W999951cXFxUp04d7d+/XytWrNCqVav0+eef65133tHYsWM1fvz4Qu03x+7duxUcHGwLVB11tc+so2GJI3Lei5EjRyo6OjrPPlcGujkefvhhzZkzR6+88ooaNmzo0Nl8+XFxcVG7du2KvJ0c9evXd2h7tWrVsvW777775OLiolGjRumuu+7K88ygy02aNEljxozRY489pokTJyogIEBms1nDhg0r1Ge9MBydI/LSo0cP/fLLL3ruuefUqFEj+fj4yGq1qkOHDoWuP7/5Mme7H330kSpXrpyrn6vr//5JMmzYMHXp0kVffvmlVq9erTFjxmjy5Mlat26doqKiClUfAOD6RSgFALju3XfffXrvvfe0adMmu0vt8lK9enVZrVb99ddftjNRpEuLTcfHx6t69erFWpvVatXhw4dtZzRI0oEDByTJtijv559/Lg8PD61evVoWi8XWb+HChUXef5UqVfT000/r6aefVmxsrBo3bqxXX31VHTt2tB3r/v37bXchy7F///5iey3Wr1+vc+fO6YsvvtCdd95pa7/yjmeRkZGSLoU2hQkiqlevru+//15JSUl2Z0vlXB54+fGYzWbdc889uueeezRt2jRNmjRJL730kn744Qfbvr29vdWzZ0/17NlTGRkZ+uc//6lXX31Vo0ePtrsEqiA2bdqkQ4cO2c4Sy6lr//79ufpeWXfO2SXx8fF2/a52JtXJkyeVkpJid3bNlZ+9K+WcgeTm5lbg96BVq1aqVq2a1q9fr9dff71AY693L730kubNm6eXX37ZdrfLq1m+fLnuuusuzZ8/3649Pj5elSpVytU/5yyhHIZh6ODBg2rQoIFde2HezxyOftcvXLigtWvXavz48Ro7duxVawwKCpKvr2+B/xhwNTnf/eDgYIc+d5GRkRoxYoRGjBihv/76S40aNdLUqVP18ccfF0s9AIDrB5fvAQCue88//7y8vb01YMAAnTlzJtfzhw4d0syZMyVJnTp1kiTNmDHDrs+0adMkKded7orDrFmzbL8bhqFZs2bJzc1N99xzj6RLZxGYTCa7M16OHj2qL7/8stD7zM7OznWpUHBwsEJDQ5Weni5Jatq0qYKDgzVnzhxbm3Tptux79+4tttci5yyJy88iy8jI0DvvvGPXr3HjxoqIiNCMGTNyBS+OnIHWqVMnZWdn273ekjR9+nSZTCbb2WHnz5/PNbZRo0aSZHsdzp07Z/e8u7u76tatK8MwlJmZmW8teTl27Jj69esnd3d3Pffcc3Z1b968WZs2bbK1paSk6L333lN4eLjtjKOcf7j/9NNPtn7Z2dl677338txfVlaW5s6da3uckZGhuXPnKigoSE2aNMlzTHBwsNq2bau5c+fq1KlTuZ6Pi4u76vGZTCa99dZbGjduXJ6XxJVl/v7+evLJJ7V69Wrt2LHjmn1dXFxyfV4/++yzPNfikqQPP/zQ7vLj5cuX69SpU7nOZizM+5nD0e96Xt9VKfd8aTab1a1bN3399dfaunVrrv0V9IzR6Oho+fr6atKkSXl+v3I+d6mpqUpLS7N7LjIyUhUqVLA7LgBA+cGZUgCA615kZKQ++eQT9ezZU3Xq1FGfPn1Ur149ZWRk6JdfftFnn32mfv36SZIaNmyovn376r333rNdVrZ582YtWrRI3bp101133VWstXl4eGjVqlXq27evmjVrpm+//VYrV67Uiy++aFufqXPnzpo2bZo6dOighx9+WLGxsZo9e7Zq1qypXbt2FWq/SUlJuummm9S9e3c1bNhQPj4++v7777VlyxZNnTpV0qWzYV5//XX1799fbdq0Ua9evWy3iQ8PD9fw4cOL5TVo0aKFKlasqL59+2ro0KEymUz66KOPcv3D1Ww2691331WXLl3UqFEj9e/fX1WqVNG+ffu0Z88erV69+pr76dKli+666y699NJLOnr0qBo2bKjvvvtOX331lYYNG2YLdSZMmKCffvpJnTt3VvXq1RUbG6t33nlHN910k22x8XvvvVeVK1dWy5YtFRISor1792rWrFnq3LmzQ4vq//777/r4449ltVoVHx+vLVu26PPPP7cd++VnwYwaNUqffvqpOnbsqKFDhyogIECLFi3SkSNH9Pnnn9suU7z11lt1xx13aPTo0Tp//rxtYfGsrKw8awgNDdXrr7+uo0eP6uabb9bSpUu1Y8cOvffee3Jzc7tq7bNnz1arVq1Uv359DRw4UDVq1NCZM2e0adMm/fe//9XOnTuvOrZr167q2rVrvq+PdGn9qn/961+52qOiogoViE6bNk1eXl52bWazWS+++GKBt5WXZ599VjNmzNBrr72mJUuWXLXffffdpwkTJqh///5q0aKF/vjjDy1evPiq62AFBASoVatW6t+/v86cOaMZM2aoZs2aGjhwoF2/wr6fkuPfdV9fX9s6TZmZmQoLC9N3332X66xG6dJlit99953atGmjJ554QnXq1NGpU6f02WefacOGDbluVnAtvr6+evfdd/Xoo4+qcePGeuihhxQUFKTjx49r5cqVatmypWbNmqUDBw7onnvuUY8ePVS3bl25urrq3//+t86cOaOHHnrI4f0BAMqQ0rjlHwAAhXHgwAFj4MCBRnh4uOHu7m5UqFDBaNmypfH222/b3d4+MzPTGD9+vBEREWG4ubkZVatWNUaPHm3XxzCuftt6ScbgwYPt2nJuJX/5rcpzbml/6NAh49577zW8vLyMkJAQY9y4cbluGT9//nyjVq1ahsViMW655RZj4cKFtlvd57fvy58bN26cYRiXbhf/3HPPGQ0bNjQqVKhgeHt7Gw0bNjTeeeedXOOWLl1qREVFGRaLxQgICDB69+5t/Pe//7Xrk3MsV8qrxrxs3LjRuOOOOwxPT08jNDTUeP75543Vq1cbkowffvjBru+GDRuM9u3b2+pu0KCB8fbbb+dbi2FcurX88OHDjdDQUMPNzc2oVauW8eabb9rdjn7t2rVG165djdDQUMPd3d0IDQ01evXqZRw4cMDWZ+7cucadd95pBAYGGhaLxYiMjDSee+45IyEh4ZrHmfM5yPlxdXU1AgICjGbNmhmjR482jh07lue4Q4cOGd27dzf8/f0NDw8P4/bbbzdWrFiRZ7927doZFovFCAkJMV588UVjzZo1uV7HNm3aGLfeequxdetWo3nz5oaHh4dRvXp1Y9asWXnWu3Dhwlz76dOnj1G5cmXDzc3NCAsLM+677z5j+fLltj4//PCDIcn47LPPrvma5NRyuerVq9u9Tpf/PP7444ZhXPt9vlzOZzCvHxcXlwLVmtf3+HL9+vUzXFxcjIMHD151G2lpacaIESOMKlWqGJ6enkbLli2NTZs2GW3atDHatGlj65dT06effmqMHj3aCA4ONjw9PY3OnTvn+pw4+n7md5yOfNf/+9//Gvfff7/h7+9v+Pn5GQ8++KBx8uRJu/klx7Fjx4w+ffoYQUFBhsViMWrUqGEMHjzYSE9PNwzDMBYuXGhIMrZs2ZJnnVd+93/44QcjOjra8PPzMzw8PIzIyEijX79+xtatWw3DMIyzZ88agwcPNm655RbD29vb8PPzM5o1a2YsW7bsqu8HAKBsMxlGIVZsBQAA6tevn5YvX2672xoA5Fi/fr3uuusuffbZZ+revfs1+7Zt21Znz54ttjWcAAAoK1hTCgAAAAAAAE5HKAUAAAAAAACnI5QCAAAAAACA07GmFAAAAAAAAJyOM6UAAAAAAADgdIRSAAAAAAAAcDrX0i7A2axWq06ePKkKFSrIZDKVdjkAAAAAAADlimEYSkpKUmhoqMzmq58PdcOFUidPnlTVqlVLuwwAAAAAAIBy7e+//9ZNN9101edvuFCqQoUKki69ML6+vqVcTdFYrVbFxcUpKCjomskjAODqmEsBoOiYSwGg6MrTXJqYmKiqVavaMpirueFCqZxL9nx9fctFKJWWliZfX98y/4EFgNLCXAoARcdcCgBFVx7n0vyWTSofRwkAAAAAAIAyhVAKAAAAAAAATkcoBQAAAAAAAKe74daUclR2drYyMzNLu4xrslqtyszMVFpaWrm53vR64ebmJhcXl9IuAwAAAACAcotQ6gqGYej06dOKj48v7VLyZRiGrFarkpKS8l08DAXn7++vypUr89oCAAAAAFACCKWukBNIBQcHy8vL67oOJAzDUFZWllxdXa/rOssawzCUmpqq2NhYSVKVKlVKuSIAAAAAAMofQqnLZGdn2wKpwMDA0i4nX4RSJcfT01OSFBsbq+DgYC7lAwAAAACgmLEQ0WVy1pDy8vIq5UpwPcj5HFzva4sBAAAAAFAWEUrlgbOOIPE5AAAAAACgJBFKAQAAAAAAwOkIpeB0X375pWrWrCkXFxcNGzbsqm0AAAAAAKD8IpQqB8xms0wm01V/XnnlldIu0c6TTz6p7t276++//9bEiROv2lYU69evl8lkUnx8fJG3BQAAAAAAih933ysHTp48aVv/aOnSpRo7dqz2799ve97Hx8f2u2EYys7Olqtr6bz1ycnJio2NVXR0tEJDQ6/aBgAAAAAAyjfOlCoHKleubPvx8/OTyWSyPd63b58qVKigb7/9Vk2aNJHFYtGGDRvUr18/devWzW47w4YNU9u2bW2PrVarJk+erIiICHl6eqphw4Zavnz5NWtJT0/XyJEjFRYWJm9vbzVr1kzr16+XdOnspQoVKkiS7r77bplMpqu2SdKGDRvUunVreXp6qmrVqho6dKhSUlLs9vXCCy+oatWqslgsqlmzpubPn6+jR4/qrrvukiRVrFhRJpNJ/fr1K/wLDAAAAAAAih2h1A1i1KhReu2117R37141aNDAoTGTJ0/Whx9+qDlz5mjPnj0aPny4HnnkEf34449XHTNkyBBt2rRJS5Ys0a5du/Tggw+qQ4cO+uuvv9SiRQvbGVyff/65Tp06ddW2Q4cOqUOHDnrggQe0a9cuLV26VBs2bNCQIUNs++rTp48+/fRTvfXWW9q7d6/mzp0rHx8fVa1aVZ9//rkkaf/+/Tp16pRmzpxZ2JcOAAAAAACUAC7fc9CZn4/rzIbj+fbzCqugmn0a2rUd/HCnUk8k5Ts2pFU1hbSuVugar2XChAlq3769w/3T09M1adIkff/992revLkkqUaNGtqwYYPmzp2rNm3a5Bpz/PhxLVy4UMePH7ddhjdy5EitWrVKCxcu1KRJkxQcHCxJCggIUOXKlSUpz7bJkyerd+/etkXPa9Wqpbfeektt2rTRu+++q+PHj2vZsmVas2aN2rVrZ6svR0BAgG3b/v7+Dh83AAAAAABwDkIpB2WnZykzMT3ffll+ltxtyRkOjc1OzypUbY5o2rRpgfofPHhQqampuYKsjIwMRUVF5Tnmjz/+UHZ2tm6++Wa79vT0dAUGBhZo/zt37tSuXbu0ePFiW5thGLJarTpy5Ij++OMPubi45BmOAQAAAACA6x+hlINcLK5y880dOF3J1cc9zzZHxrpYSu7t8Pb2tntsNptlGIZdW2Zmpu335ORkSdLKlSsVFhZm189iyftYkpOT5eLiom3btsnFxcXuucsXW3dEcnKynnzySQ0dOjTXc9WqVdPBgwcLtD0AAAAAAHB9IZRyUEjrwl9ad+XlfNeDoKAg7d69265tx44dcnNzkyTVrVtXFotFx48fd/hspKioKGVnZys2NlatW7cuUn2NGzfWn3/+qZo1a+b5fP369WW1WvXjjz/aLt+7nLv7pXAwOzu7SHUAAAAAAICSQSh1g7r77rv15ptv6sMPP1Tz5s318ccfa/fu3bZL8ypUqKCRI0dq+PDhslqtatWqlRISErRx40b5+vqqb9++ubZ58803q3fv3urTp4+mTp2qqKgoxcXFae3atWrQoIE6d+7scH0vvPCC7rjjDg0ZMkQDBgyQt7e3/vzzT61Zs0azZs1SeHi4+vbtq8cee0xvvfWWGjZsqGPHjik2NlY9evRQ9erVZTKZtGLFCnXq1Emenp4FPlsLAAAAAIDidm7VNJ1bNc2uzTAMybAqOztbCS4uksksk8lk1yewQ4wCO8Q4s9QSx933blDR0dEaM2aMnn/+ed12221KSkpSnz597PpMnDhRY8aM0eTJk1WnTh116NBBK1euVERExFW3u3DhQvXp00cjRoxQ7dq11a1bN23ZskXVqhXsLLMGDRroxx9/1IEDB9S6dWtFRUVp7NixtgXUJendd99V9+7d9fTTT+uWW27RwIEDlZKSIkkKCwvT+PHjNWrUKIWEhNjdtQ8AAAAAgNKSfTFRWRdO2P1kx59UdsJpKTlO2QmnlR1/Mnefi4mlXXqxMxlXLixUziUmJsrPz08JCQny9fW1ey4tLU1HjhxRRESEPDw8SqlCxxmGoaysLLm6uuZKUFF0Ze3zAKBwrFarYmNjFRwcLLOZv9UAQGEwlwKA43LOlLJmpsmafF7StWIZk8w+ATK7eZSpM6Wulb1cjsv3AAAAAAAAnCSwQ4wsYbfq+LTOkskkXetcIZNJ1tR43RSzUj71o51XpJPwZwwAAAAAAAAnyU6J199vP3ApjDKs1+5sWCXD0N9vP6DslHin1OdMhFIAAAAAAABOEr9xkYz01PwDqRyGVUZGquI3fliyhZUCQikAAAAAAAAnMAxD59e8Xaix59e8pfK2LDihFAAAAAAAgBNkJ59TZuwhXXtx8zwYhjJjDyk75XyJ1FVaCKUAAAAAAACcwJqWXLTxF5OKqZLrA6EUAAAAAACAE5g9fIo23rNCMVVyfXAt7QLKg2k/HtL0nw7btRmGIashGTJkkklmk2Qymez6DL+zhmLaRDqzVAAAAAAAUEpcfALlFhypzLjDl+6+5yiTSW5BNeTiHVByxZUCQqlikJiWpRMJaYUaBwAAAAAAbgwmk0kB7Z/RmcXDCzw2oP3QXCe7lHVcvlcMfD1cFebnoUAvN+X38TBJCvRyU5ifh3w9yARL09GjR2UymbRjx47SLgUAAAAAcIPwb9lXJouXZHIwkjGZZXL3kn/LPiVbWCkglCoGMW0i9X6Phoq/mKX8QkuTSYq/mKX3ezTk0j0AAAAAAG4w8T8vkMnNQzKsjg0wrDK5eSj+5wUlW1gpIJQqBvEXM9V90VYZurSO1LXkrDPVfdFWxV/MdE6BecjMLL19l7SMjIzSLgEAAAAAgDxlX0yUNflcgcZYk88p+2JiCVVUegilisGirX8rNSM730Aqh9WQUjOy9eHWv4u1DqvVqjfeeEM1a9aUxWJRtWrV9Oqrr9ouU1u6dKnatGkjDw8PLV68WJL0/vvvq06dOvLw8NAtt9yid955x+H9/fLLL2rUqJE8PDzUtGlTffnll7kuh9u9e7c6duwoHx8fhYSE6NFHH9XZs2dtz7dt21ZDhw7V888/r4CAAFWuXFmvvPKK3X7i4+M1YMAABQUFydfXV3fffbd27txpe/6VV15Ro0aN9P777ysiIkIeHh6SpFWrVqlVq1by9/dXYGCg7rvvPh06dKgQrywAAAAAAMXDxdNXrhXD5FoxTC5+VWT29JPMLvadzC4ye/rJxb/K//p6+pZOwSWIRY2KyDAMzdpwpFBj395wRM+0iii2hcpGjx6tefPmafr06WrVqpVOnTqlffv22Z4fNWqUpk6dqqioKFswNXbsWM2aNUtRUVHavn27Bg4cKG9vb/Xt2/ea+0pMTFSXLl3UqVMnffLJJzp27JiGDRtm1yc+Pl533323BgwYoOnTp+vixYt64YUX1KNHD61bt87Wb9GiRYqJidFvv/2mTZs2qV+/fmrZsqXat28vSXrwwQfl6empb7/9Vn5+fpo7d67uueceHThwQAEBl+48cPDgQX3++ef64osv5OJy6cuckpKimJgYNWjQQMnJyRo7dqzuv/9+7dixQ2YzeSwAAAAAwPkCO8QosEOMXZthGMpKOqvY/x5V8E3hcq1Qqdwtap4XQqkiOpeaoUPnUgs8zpB06FyqzqdmKtDbvch1JCUlaebMmZo1a5YtUIqMjFSrVq109OhRSdKwYcP0z3/+0zZm3Lhxmjp1qq0tIiJCf/75p+bOnZtvKPXJJ5/IZDJp3rx58vDwUN26dXXixAkNHDjQ1icn7Jo0aZKtbcGCBapataoOHDigm2++WZLUoEEDjRs3TpJUq1YtzZo1S2vXrlX79u21YcMGbd68WbGxsbJYLJKkKVOm6Msvv9Ty5cv1xBNPSLp0yd6HH36ooKAg274eeOABu5oXLFigoKAg/fnnn6pXr57jLy4AAAAAACXIZDLJxSdQLgHZcvEJvCECKYlQqsiS07OLND4pPatYQqm9e/cqPT1d99xzz1X7NG3a1PZ7SkqKDh06pMcff9wuSMrKypKfn1+++9u/f78aNGhgu1ROkm6//Xa7Pjt37tQPP/wgHx+fXOMPHTpkF0pdrkqVKoqNjbVtIzk5WYGBgXZ9Ll68aHcpXvXq1e0CKUn666+/NHbsWP322286e/asrNZLi8gdP36cUAoAAAAAgFJGKFVEPhaX/DtdQwVL8bwFnp6e+fbx9va2/Z6cnCxJmjdvnpo1a2bXL+fyt6JKTk5Wly5d9Prrr+d6rkqVKrbf3dzc7J4zmUy2ACk5OVlVqlTR+vXrc23D39/f9vvlx5ajS5cuql69uubNm6fQ0FBZrVbVq1ePhdABAAAAALgOEEoVUaCXuyIDvXT4XKocXOdckmSSVCPQSwFebvn2dUStWrXk6emptWvXasCAAfn2DwkJUWhoqA4fPqzevXsXeH+1a9fWxx9/rPT0dNtldVu2bLHr07hxY33++ecKDw+Xq2vhPmqNGzfW6dOn5erqqvDwcIfHnTt3Tvv379e8efPUunVrSdKGDRsKVQMAAAAAACh+rPZcRCaTSUNaRRRqbHEucu7h4aEXXnhBzz//vD788EMdOnRIv/76q+bPn3/VMePHj9fkyZP11ltv6cCBA/rjjz+0cOFCTZs2Ld/9Pfzww7JarXriiSe0d+9erV69WlOmTJEk2zENHjxY58+fV69evbRlyxYdOnRIq1evVv/+/ZWd7dhlj+3atVPz5s3VrVs3fffddzp69Kh++eUXvfTSS9q6detVx1WsWFGBgYF67733dPDgQa1bt04xMTFX7Q8AAAAAAJyLUKoY9G1aVV7uLjI7mC+ZTZKXu4v6NK1arHWMGTNGI0aM0NixY1WnTh317NnTtjZTXgYMGKD3339fCxcuVP369dWmTRt98MEHiojIP2Tz9fXV119/rR07dqhRo0Z66aWXNHbsWEmyrTMVGhqqjRs3Kjs7W/fee6/q16+vYcOGyd/f3+G735lMJn3zzTe688471b9/f91888166KGHdOzYMYWEhFx1nNls1pIlS7Rt2zbVq1dPw4cP15tvvunQPgEAAAAAQMkzGYZRkKvOyrzExET5+fkpISFBvr6+ds+lpaXpyJEjioiIsFvA2xGr98fqvvc3y5Ah6zVeUbNJMsmklQNu1721gwtzCDaGYSgrK0uurq7Xxcr8ixcvVv/+/ZWQkODQGlfXu6J8HgCUHVarVbGxsQoODnY4MAcA2GMuBYCiK09z6bWyl8uV7aO8Tkz78ZAGLNspf09X5RfxGYbk7+mqx5ft1LQfD12783Xuww8/1IYNG3TkyBF9+eWXeuGFF9SjR49yEUgBAAAAAICSRShVDBLTsnQiIU3nUjPzXezckHQuNVMnEtKUmJbljPIKZdKkSfLx8cnzp2PHjpKk06dP65FHHlGdOnU0fPhwPfjgg3rvvfdKuXIAAAAAAFAWcPe9YuDr4aowP/vLuwzj0mV8hgyZZLp02d4Vl9j5ely/L/+gQYPUo0ePPJ/LORPq+eef1/PPP+/MsgAAAAAAQDlRqqnITz/9pDfffFPbtm3TqVOn9O9//1vdunW7av9Tp05pxIgR2rp1qw4ePKihQ4dqxowZTqv3amLaRCqmTWRpl1GsAgICFBAQUNplAAAAAACAcqpUL99LSUlRw4YNNXv2bIf6p6enKygoSC+//LIaNmxYwtUBAAAAAACgpJTqmVIdO3a0rU/kiPDwcM2cOVOStGDBgpIqCwAAAAAAACXs+l3UqJikp6crPT3d9jgxMVHSpVstWq1Wu75Wq1WGYdh+yoKcOstKvWVJzucgr88KgPIjZ+7new4AhcdcCgBFV57mUkePodyHUpMnT9b48eNztcfFxSktLc2uLTMzU1arVVlZWcrKcvzOeEk73lLyjrft2i6FRFbJMCSTSZI510LnPo2eUYVGQx3ez5UMw1B2drak3Iuoo+iysrJktVp17tw5ubm5lXY5AEqI1WpVQkKCDMOQ2cxNaQGgMJhLAaDoytNcmpSU5FC/ch9KjR49WjExMbbHiYmJqlq1qoKCguTr62vXNy0tTUlJSXJ1dZWrawFemqwUZaecLHhxWSkF289VEJiUDFdXV5nNZgUGBsrDwyP/AQDKJKvVKpPJpKCgoDL/H38AKC3MpQBQdOVpLnX039DlPpSyWCyyWCy52s1mc6432Wy+dDZTzo+jXCy+cvEJk5GVJmvaeUnXupTOJLNHgEyuHnKx+BbpDCfDMGzjOVMqf+vXr9ddd92lCxcuyN/fP9/+OZ+DvD4rAMoXvusAUHTMpQBQdOVlLnW0/nIfSjmDX+NhcguoqzNfdZVkUn6hlDU9XiEdvpJX+L1OqhAAAAAAAOD6UqrRW3Jysnbs2KEdO3ZIko4cOaIdO3bo+PHjki5detenTx+7MTn9k5OTFRcXpx07dujPP/90dul2stPiFbuy56X1o5TfYl6X1pmKXdlT2WnxTqgub5mZmaW272vJyMgo7RIAAAAAAIATlGootXXrVkVFRSkqKkqSFBMTo6ioKI0dO1aSdOrUKVtAlSOn/7Zt2/TJJ58oKipKnTp1cnrtl0ve+5GMzFTlH0jlsMrITFXy3o+LtQ6r1ao33nhDNWvWlMViUbVq1fTqq6/q6NGjMplMWrp0qdq0aSMPDw8tXrxYkvT++++rTp068vDw0C233KJ33nnHoX3lbPOLL77QXXfdJS8vLzVs2FCbNm2y6/f555/r1ltvlcViUXh4uKZOnWr3fHh4uCZOnKg+ffrI19dXTzzxhD744AP5+/trxYoVql27try8vNS9e3elpqZq0aJFCg8PV8WKFTV06FDbQu+S9NFHH6lp06aqUKGCKleurIcfflixsbFFfFUBAAAAAEBJKNXL99q2bfv/d6nL2wcffJCr7Vr9S4NhGErcMbtQYxN3zJJvo8HFth7U6NGjNW/ePE2fPl2tWrXSqVOntG/fPtvzo0aN0tSpUxUVFWULpsaOHatZs2YpKipK27dv18CBA+Xt7a2+ffs6tM+XXnpJU6ZMUa1atfTSSy+pV69eOnjwoFxdXbVt2zb16NFDr7zyinr27KlffvlFTz/9tAIDA9WvXz/bNqZMmaKxY8dq3LhxkqSff/5Zqampeuutt7RkyRIlJSXpn//8p+6//375+/vrm2++0eHDh/XAAw+oZcuW6tmzp6RLZ39NnDhRtWvXVmxsrGJiYtSvXz998803xfL6AgAAAACA4sOaUkVkTTunrITDhRhpKCvhsKxp5+XiGVjkOpKSkjRz5kzNmjXLFihFRkaqVatWOnr0qCRp2LBh+uc//2kbM27cOE2dOtXWFhERoT///FNz5851OJQaOXKkOnfuLEkaP368br31Vh08eFC33HKLpk2bpnvuuUdjxoyRJN188836888/9eabb9qFUnfffbdGjBhhe/zzzz8rMzNT7777riIjIyVJ3bt310cffaQzZ87Ix8dHdevW1V133aUffvjBFko99thjtm3UqFFDb731lm677TYlJyfLx8enIC8nAAAAAAAoYWV7OffrgDUjuYjjk4qljr179yo9PV333HPPVfs0bdrU9ntKSooOHTqkxx9/XD4+Praff/3rXzp06JDD+23QoIHt9ypVqkiS7ZK5vXv3qmXLlnb9W7Zsqb/++svusrvL68rh5eVlC6QkKSQkROHh4XbhUkhIiN3ledu2bVOXLl1UrVo1VahQQW3atJGkXJeAAgAAAACA0seZUkVkdi/aGThm9wrFUoenp2e+fby9vW2/JydfCtPmzZunZs2a2fVzcXFxeL9ubm6233MuQ7RaHV1bK3ddeW03Z9t5teXsKyUlRdHR0YqOjtbixYsVFBSk48ePKzo6msXTAQAAAAC4DhFKFZHZI1CufjWUlXBEUkHWuzLJ1S9CZo+AYqmjVq1a8vT01Nq1azVgwIB8+4eEhCg0NFSHDx9W7969i6WGK9WpU0cbN260a9u4caNuvvnmAgVfjti3b5/OnTun1157TVWrVpV0aSF9AAAAAABwfSKUKiKTySTfRoN1/seRBR7r22hIsS1y7uHhoRdeeEHPP/+83N3d1bJlS8XFxWnPnj1XvaRv/PjxGjp0qPz8/NShQwelp6dr69atunDhgmJiYopc04gRI3Tbbbdp4sSJ6tmzpzZt2qRZs2Y5fIe/gqhWrZrc3d319ttva9CgQdq9e7cmTpxY7PsBAAAAAADFgzWlioFPnUdlcvOS4y+nWSY3L/nUeaRY6xgzZoxGjBihsWPHqk6dOurZs6fdmktXGjBggN5//30tXLhQ9evXV5s2bfTBBx8oIiKiWOpp3Lixli1bpiVLlqhevXoaO3asJkyYYLfIeXEJCgrSBx98oM8++0x169bVa6+9pilTphT7fgAAAAAAQPEwGYZRkGvOyrzExET5+fkpISFBvr6+ds+lpaXpyJEjioiIkIeHR4G2m3r0O535qqtkGJKutaaSWTKZFNLtP/Kq3r7gB3AZwzCUlZUlV1fXYjvjCv9TlM8DgLLDarUqNjZWwcHBMpv5Ww0AFAZzKQAUXXmaS6+VvVyubB/ldSLh9xk6+/2TMlv8lf+6UobMFn+dXfOEEn6fUfLFAQAAAAAAXIcIpYqBNT1R2cknZE07J0dCKWvauUv90xOdUV6hTJo0ST4+Pnn+dOzYsbTLAwAAAAAAZRwLnRcDs8VXLj5hdm1GzmV8hiGZTJLMuS6xM1uufgpbaRs0aJB69OiR53Oenp5OrgYAAAAAAJQ3hFLFwK/xMPk1HlbaZRSrgIAABQQElHYZAAAAAACgnOLyPQAAAAAAADgdoRQAAAAAAACcjlAKAAAAAAAATseaUsVg+u4fNX3PT3ZthmHIKsO2zrlZplwLnQ+/9U4Nr9fGmaUCAAAAAABcFwilikFiZppOpCYUahwAAAAAAMCNiMv3ioGvm4fCvPwUaPGSKZ++JkmBFi+FefnJ182j2GowDENPPPGEAgICZDKZtGPHjlx92rZtq2HDhhXbPgEAAAAAAAqLUKoYDK/XRvNa9VB8xkWZ8omlTDIpPuOi5rXqUayX7q1atUoffPCBVqxYoVOnTqlevXrFtu3ScPTo0auGawAAAAAAoOwjlCoG8ekX9eC6RTIMySrjmn1z1pl6cN0ixadfLLYaDh06pCpVqqhFixaqXLmyXF25MhMAAAAAAFy/CKWKwYcHtyo1KyPfQCqHVYZSszL00aGtxbL//v3765lnntHx48dlMpkUHh6ulJQU9enTRz4+PqpSpYqmTp1aoG2mp6dr5MiRCgsLk7e3t5o1a6b169dLktLS0nTrrbfqiSeesPU/dOiQKlSooAULFkiSzp07p169eiksLExeXl6qX7++Pv30U7t9WK1WvfHGG6pZs6YsFouqVaumV199VZIUEREhSYqKipLJZFLbtm0L+eoAAAAAAIDrEaFUERmGoVl7NxRq7Nt/bpBhOBZkXcuMGTM0YcIE3XTTTTp16pS2bNm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BSdkyWdPz7etn8c11rOdNx5Rpzf+vF4E+Aap42dg0c4rirf91qMbgwCC7v16Yj2Up3Rqf7zgPs1euelPd4uTiQBob4OGfa+wZR98b/0qqEOxve5yU4q5ka/5/HZKUa5/ZHimyWvP/cldwy/3eJJhPKM2a/1l0AT4VVemysZmJ6TpvdeyzFBRQye6vF26nDF20ns93nJvJPVe9aZbzMlvz/5+OAItfrrFx8pbVmv9fLwJ9A+UXfNlfL7ISlWjN/69DkhQUFGT31wt5pSvTmv8ker3MEZbzLkqxxuU7ziyXXPVmWhIla/7/01Ge5whDhi5aM+VpdZNJJuYI5ohcmCNu7DniSswRec8RV86lzBHMEZdjjmCOuBJzRN5zxOVzqXcZnyM8PDwc2p/JKOXbszVr1ky333673n77bUmXTvGqVq2ahgwZolGjRuU7Pjs7W7feeqs6deqkadOm5ds/MTFRfn5+SkhIkK+vb779rwfxFzNVdeIaXczMltWBd8tskjzdXPT3mPby93Qr+QIBoAyz/v9fQ4ODg2U2l/r9PwCgTGIuBYCiK09zqaPZS6kfZUxMjObNm6dFixZp7969euqpp5SSkqL+/ftLkvr06aPRo0fb+k+YMEHfffedDh8+rN9//12PPPKIjh07pgEDBpTWIZQ4f083Le/b9NJlJde+4lBmk2SSSZ/3bUogBQAAAAAArlulvqZUz549FRcXp7Fjx+r06dNq1KiRVq1aZVv8/Pjx43YJ4YULFzRw4ECdPn1aFStWVJMmTfTLL7+obt26pXUIThFdO1grBtyu7ou2KjXj0umFl580lZNVebq56PO+TXVv7eBc2wAAAAAAALhelPrle85WFi/fu1z8xUx9uPVvzfz5iI6c/981wpGBXnqmVYT6Nq0qP86QAgCHlafTpAGgtDCXAkDRlae51NHspdTPlELB+Hu6aWjrGnrstqryfXmVJOnIi3epWkVvmUz5XNsHAAAAAABwnSCUKgOm/XhI0386bNd2+QluLWf9kmcgNfzOGoppE1ni9QEAAAAAABQUoVQZkJiWpRMJV7/V6snE9KuOAwAAAAAAuB4RSpUBvh6uCvPzyPM5a3a2zC4uVx0HAAAAAABwPSK1KANi2kTmeRleeVoEDQBKWsLvM5Tw+8zcTxhStjVb/zW7/O9Wppfxa/ys/BoPK/H6AAAAgBsNoRQA4IZgTU9UdvKJqz6ffY1xAAAAAIofoRQA4IZgtvjKxSfMvtEwlJ1yUpLk4l1FMuU+69RsufotbAEAAAAUHqEUAOCG4Nd4WK7L8KyZKTo2u6IkKbTPbrlaKpRCZQAAAMCNiYWIAAAAAAAA4HSEUgAAAAAAAHA6QikAAAAAAAA4HaEUAOCGZRiG7ffsi+fsHgMAAAAoWYRSAIAbTnZavBK2v62THzextZ384Gb994M6Stj+trLT4kuvOAAAAOAGQSgFALihpB79Tn/Pj9D5H0cqK/Go3XNZCUd0/seR+nt+hFKPflc6BQIAAAA3CEIpAMANI/XodzrzVVcZmRclGf//c7lLbUbmRZ35qivBFAAAAFCCCKWA/2vvzuOqKtf//782g4AgoIIDSuCMiiMamOBxHlIkhyzPMW0gLRXSyswsT6ViltnPqQKttDRLS0zzaKZkilYqjmnJB02cUEMUUAZh7/39g99eiUN1Tskg7+c/5dp77X0vHvtxrXVf931ft4hUCOa8S5xf9wBYrYDlD95tAauV8+se0FI+EREREZHbREkpERGpEC7/9BHWghz+OCFlY8FakMPln5bezmaJiIiIiFRYSkqJiMgdz2q1krVvwf90bta++dqVT0RERETkNlBSSkRE7niWvAsUZh7jxhpSf8RKYeYxLHkZt6NZIiIiIiIVmpJSIiJyx7NcvfwXz8/+m1oiIiIiIiI2SkqJiMgdz66S2188v8rf1BIREREREbFRUkpERO54ds7VcfCoD5j+yzNNOHjUx8652u1oloiIiIhIhaaklIiI3PFMJhPurcf8T+e6tx6LyfTfJrNEREREROSPKCklIiIVglvThzA5VubP3/rsMDlWxq3psNvZLBERERGRCktJKRERqRDsnT2p0fdTMJn449ufHZhM1Oi3AntnzxJonYiIiIhIxaOklIiIVBiV/XtSM+ILTI4uFNWXun5ZXtExk6MLNe9bQ2W/HiXfSBERERGRCsKhtBsgIiJSkir798T3sV+4/NNSsvbOozDrF+M1B496uLceS5VmD2Hn5FGKrRQRERERufMpKSUiIhWOvbMnHm3G4tb8YU68XbSzns8j/0cl97tU1FxEREREpIRo+Z6IiFRY1yag7J2rKSElIiIiIlKClJQSEREREREREZESp6SUiIiIiIiIiIiUONWUEhGRCiFzz/9H5p45xQ9arcb/nvkwEEw3jtV4tH0Kj7bjbnPrREREREQqHiWlRESkQrDkZ2G+fPqWr5uvpN3yPBERERER+fspKSUiIhWCnZM79m51bnzBCmaLGXs7e7hJnXM7J/fb3zgRERERkQpISSkREakQPNqOu+kyPIvFwvnz56lRowZ2diq1KCIiIiJSUvT0LSIiIiIiIiIiJU5JKRERERERERERKXFKSomIiIiIiIiISIlTUkpEREREREREREqcklIiIiIiIiIiIlLilJQSEREREREREZESp6SUiIiIiIiIiIiUOCWlRERERERERESkxCkpJSIiIiIiIiIiJU5JKRERERERERERKXFKSomIiIiIiIiISIlTUkpEREREREREREqcklIiIiIiIiIiIlLilJQSEREREREREZESp6SUiIiIiIiIiIiUOCWlRERERERERESkxCkpJSIiIiIiIiIiJU5JKRERERERERERKXFKSomIiIiIiIiISIlzKO0GlDSr1QpAVlZWKbfkr7NYLGRnZ+Ps7IydnfKLIiL/C8VSEZG/TrFUROSvu5NiqS3nYsvB3EqFS0plZ2cD4OvrW8otERERERERERG5c2VnZ+Ph4XHL103WP0pb3WEsFgtnzpyhSpUqmEym0m7OX5KVlYWvry8nT57E3d29tJsjIlIuKZaKiPx1iqUiIn/dnRRLrVYr2dnZ+Pj4/O6srwo3U8rOzo66deuWdjP+Vu7u7uX+BysiUtoUS0VE/jrFUhGRv+5OiaW/N0PKpnwvUhQRERERERERkXJJSSkRERERERERESlxSkqVY05OTvz73//GycmptJsiIlJuKZaKiPx1iqUiIn9dRYylFa7QuYiIiIiIiIiIlD7NlBIRERERERERkRKnpJSIiIiIiIiIiJQ4JaVEREREROR3WSyW0m6CiIjcgZSUKgdmzZpFeHh4aTdDRKRce//9941YqnKKIiL/HTu737oNiqEiIvJ3UVKqHPDy8uKbb77h119/Le2miIiUW/b29qxbt478/HxMJlNpN0dEpFxZs2YNTzzxBFarVTFURET+NkpKlTEWi+WG6dE9evTg6tWrHDx4sJRaJSJSvlit1htiaYcOHXBxcSExMbGUWiUiUn6YzWbMZrPxb1dXV+Li4khPTycxMZHY2NhSbJ2ISPl34sQJ5syZw5EjR4CKOwtVSakywtZ5srOzKzY9GqBOnTo0atSITZs2lUbTRETKDVssNZlMN8TS2rVr07x5c9asWQNU3Bu/iMifYW9vj729vfHvb7/9FpPJRN26dQkPD2ffvn1cvny5FFsoIlI+2J45jx07xqlTp4zj77//PnPnzqVevXpYLJYKOwtVSalScu3IE/y2Tv/bb79l1qxZbN26lcLCQuP1nj178vXXXxc7JiJSkd0sqWSLpYcOHeKDDz5gx44dxmuVK1emS5cubNy4scTaKCJSVt0shtqOZWZmsnjxYoYOHcq4ceOAoqS/u7s7Dz74IBcvXuSdd97Bzc2tJJssIlKuXLlyBSgaLD148CB9+/YlPDycU6dOUVhYyOeff85TTz1FpUqVbhhMrUgq7pWXMtvI09mzZwF47733aNiwIcOGDWP16tWMHDmSyMhI4/19+vTh4MGDnDlzplTaKyJSVtg6TdePJhUUFLBixQoCAwPp1KkTb7/9No899hjR0dFAUdzt0qULycnJXLhwocKORolIxWWxWIyB0ZvFQJPJxE8//UT//v1544038PLyonbt2uzevZtp06bx1FNPsW/fPvLz80u66SIi5UJKSgpPPPEEd911F127dmXatGmYzWZatGhBUlISNWrUICwsjDfffJP09HRtaIaSUreF1WqlsLDwplvnZmVlsXv3bjZu3IijoyNDhw4FirKoU6dO5eTJkyQmJrJq1So+/PBDvv/+ewCCg4Oxs7Nj165dJXotIiKlyWq1FptZaiuwm5eXR0JCAklJScXe/8svv/D0009z6tQpdu3aRWxsLLGxsezduxeApk2b4u7uzubNm0v0OkRESoPFYik2I8rOzs4YGN27dy+JiYnk5OQYr+fk5BATE0NeXh7btm1j3rx5PPPMM7Rr1w6Azp0789NPP3H69OmSvRARkTLqwIEDPPfcc6xbtw6r1cqUKVM4evQos2bNYvjw4bz66qtMmjSJX3/9lcqVK/P5558zYMAApk+fTkhICFWrVgUqdlkJJaVuA5PJhIODA3Z2dsU6Uzk5OTz33HP06dOHRYsWsXr1aj7++GMABg8ezNChQzl16hRz5sxh0qRJAKxfv56cnBw8PDxo06YNX331Valck4hISbg+mW8ymYrNLDWZTHz99dc0bdqUESNGMGjQID777DMKCwtxdHRk8ODBPProoxQUFPDpp5/y3nvvUVBQwKpVqwCoWbMm7dq1Y/Xq1UDFfgAQkTvP9THNzs6u2IyokydPEhUVRc2aNenRowfR0dEMHDiQ9PR0AC5dusT69et59tlnqVatGgAODg7G+UFBQTg7OxuDpoqhIlIR5eXl0b9/fz7++GNeeeUV9u/fj6enJ+vXr2fLli2MHTuWIUOGMGbMGOLi4ti0aZNRPsLNzY1hw4Zx+fJlDh48yPTp04Gbz16tKJSU+h9dvyPJtVJTU5k8eTJt2rShZ8+eLFq0iPz8fCpXrkyrVq0oKCigYcOG9O3bl9q1a2O1WvHx8WHJkiX07NmTFStWEBYWxoABA1i7di3Z2dkA9OrVi61bt5Kbm1uSlyoicltdG0uvXU9/4sQJLly4wGOPPYabmxv/+Mc/WLRoEUuWLOGdd94hNTWVtm3b8vbbb3PgwAEAGjRowOrVqwkJCWH69Ol4eHgwcOBAPv/8c65evYqzszPdu3fn22+/BSr2A4CI3Hmuj2m//voro0ePZvv27QBkZGSQnp7O8uXLOXfuHKtWrSIjI4OZM2dy9epVPDw8yMrKwtXVFbhxoKBKlSqEhITw5Zdf3vT7RETuNBcuXGDlypXs3LmTgoICAJydnTl16hTDhg2jffv2fPXVV3Ts2JGff/6ZatWqcc899xjnd+rUifr167Nt2zbjWFJSEvXq1SM2NpZFixbx8MMP33SVVUWhpNR/4dqO0/U7kkDRaJHVauWll15ix44djB49mgEDBvDSSy8ZGdC2bdvi5OSEj48PAIWFhZhMJn788UemTZvG/fffz9dff82zzz7Lfffdx9GjRzl58iQA9957L8nJyaSkpJTQFYuI/L1scfJatliam5vLsmXLeOGFF/jiiy9o164dDz/8MHXr1iUxMZFOnToRHR2Ns7MzvXv3xs7OjmeeeYaCggK2bNkCFK3jnzBhAgMGDGDr1q3MnTuXQYMGcezYMX755Reg6OEgLS2N5OTkEr12EZG/w7V1oa63fft2du7cafw7ISGB5cuX06RJEwD8/f2ZNWsWXbt25ezZs+zZs4esrCw2bdpEcnIyrq6uBAYGGiP6dnZ2Rsy27bT3z3/+k7Vr1/Lcc88xfPhwnn766dt5uSIiJW737t2sW7eOBQsW0KJFC8aPH88///lPJk2aZMTfgQMHUqVKlWI1oVq2bMnx48fJzc01Ymf9+vVxcnLi0qVLxrnz5s1j1KhRdOvWjQ0bNrB9+3aCg4M5fPhwyV9sGaCk1C1YrdYbspW2jlN+fj4rV65kyJAhTJw4kWPHjgFFo0XvvPMOKSkprFu3jscff5yxY8cycuRIFi5cyKZNm2jUqBHt2rUr9sAARTf9X375hcGDB1O5cmUyMjJYvHgx2dnZJCUlYbFYaNmyJQEBAcYUaxGR8uLawrrXj6x/9NFHTJo0ienTpzNv3jxcXV2pXr06HTt2ZNeuXQwaNIjWrVvz0ksv0aFDB3799Vfj3ICAALy9vY2aUQBHjx7l4YcfxtPTk5ycHD766COuXr1q7MRXr1496tevz5EjR0rgykVE/l7X1oU6fvw4eXl5AEYtk379+rFmzRoA5s6dy6hRo/Dy8sJqteLh4YGrqysjR46kXbt2xMTEEBAQgNlsNmLksGHDWLZsGV988YVRx++HH35g6dKlQFFSKiYmhl27duHs7MwDDzxQCn8FEZG/T1paWrFd7uPj4wkPD2ft2rXEx8dz9OhRnnnmGVauXMnrr78OQFhYWLGafAChoaEUFhaSkJBQ7Hk3KSmJJk2aYG9vzzfffEN6ejodO3YEimpHf/nllyxcuJBmzZpVyGXRDn/8lorFYrEY6++v7zh9+eWXJCQk4Obmxt69e6lduzYbNmwgMTGRxYsX06hRI44dO4aDgwMJCQnMmTOHAwcOcPXqVfr27UvNmjVxd3enRYsWbNq0CfhtnX6zZs3w8vJi7Nix9OnTh127djF06FDy8/PJzMzEYrFQqVKlCps9FZHyzdaBSkpK4uzZs4SEhFC9enUArl69ysyZM2nXrh0fffQRTZo0ITc3lwYNGrBr1y5atmwJgI+PDyEhIXz11VcUFhbi4OBA9erVjfedOXOGBg0a4OPjw+jRoxk8eDBJSUkEBweTk5NjJKFq1aqlGaciUmbs2bMHs9lM+/btjSTQ70lJSWHatGmsWbMGT09PWrduTVRUFF26dGHz5s08++yzTJgwgcWLF2OxWOjevTvw20YR7733Htu2bWPVqlV06NCB7OxsAgMDjWXQo0aN4vDhwzz22GN0796dlJQUzp49y5gxY7h69SqVKlUiKiqKqKio2/63ERH5O9niYHp6OhcvXuStt97igw8+oEGDBvTq1YuXX36ZKlWqMGLECGbMmEHt2rUJDg4G4Mknn+Tw4cP85z//ISoqipCQEJydndm/fz/NmzfHYrHg7OzMgw8+yKxZs7hy5QrDhw9n+fLlAEYS6siRIzz88MMEBQUZ7bLNZoWKuSy6Qs+UMpvNNy0ICUVT9hYuXFhs9D0/P5/FixcTHx/P1KlTiYuLY+HChWRnZxMfHw+Al5cX27dv5+WXXyYoKIgVK1aQmprK0qVLadGiBQ4ODjRr1ozc3Fz27NkDFHXIAFavXk3jxo355JNP8PX1JSIigsTERJ577rliRSZvNWVbRKS03Gx26bVWr15NnTp16NGjB5MnT6Zfv37GqHyfPn2oXr06zZs3N27KLi4uBAcHc/HiRc6dOwcUJfGbNm3K5cuXi+2617p1a65evcr333+PyWRiyZIl1KhRgxkzZpCbm8vQoUNJSEjgtddeK9YmxVIRKW1Xrlxh2LBhLFy4EPj9zogtxi5YsIBz587x5ZdfsmXLFjw9PZkxYwZbt24FICYmhhkzZrB582bOnTtHixYtgKJn3JycHLZv387dd99Nhw4dANiyZQtZWVn8+OOPXLhwAVdXV+Li4liyZAl16tRh+PDh7Nixg0mTJlGpUiWjPb+327SISFlkMpk4dOgQjRo14oUXXqCwsJCNGzfy7LPPsnjxYuNY48aNqVKlCj4+PsVmUN19991cuXKF/fv34+TkRLt27YyNyGzxOyYmhr59+/L222/j7+/PpEmTGDlypDFA8MQTTxATE4Ozs3OxtlXEGVI2FWqmlG1rcTs7u2JTn6+1b98+Hn74YU6fPk3jxo2ZP38+nTp1Yt68eXTq1Ak/Pz/q1KlD69atgaIfZtOmTdm9ezdXr17F19cXb29vPvzwQ5o1a2Z87okTJ0hNTSUsLIwmTZpQUFDA2rVradu2rdGOkJAQgoODb3ggMZvNxdp6s3aLiJQm2+zSzMxMTpw4QfPmzY0k/5kzZ3j++eeJjIxkwoQJHD9+nAkTJvDkk0+SmJiIj48Pfn5+ODo6kpWVhbu7OwCNGjXCy8uLDRs2MGLECKBoJKlGjRps2bLFGLkKCAggLy/PqBHVrVs3wsLCinWe4LeZsDaKpSJSErKysli6dClLly5l3rx5BAUFGRvmuLq6cs8993DhwgVOnTpF3bp1bzlbys7Ojm3btpGYmMj69evx8vIiLy+Pli1bsmLFCuLj4+nUqROVKlWif//+2Nvbk5eXx+OPP857772Ht7c3lStXpkaNGmzevJmlS5fi5OTE2rVr6dq1KxcvXiQ1NZXq1atjZ2dH37596du37y2vy7bbtIhIWZSQkEB8fDy5ubkMGzaMzp07A9C8eXPq1q3Lf/7zHzZt2kSHDh0ICwvDarUybdo0IiIi6N69O8HBwezfv5/MzExjdn+tWrUoKCgwJpWEh4czb948Ll++jJubG1A0s3/mzJkMHToUZ2dnAgICbmibLSdxbayviDOkbCrUTCnbzdPOzo78/HxOnTrFs88+a+xud+XKFZ5++mm6d+9OWloa27dv58MPPyQ2NpYtW7bg7e1N48aNcXJy4vz588bntmjRgjNnzpCcnEx4eDh+fn5ER0eTkJBAbm4uBw4cYPr06axevRqLxUL9+vUZO3YsXbp0AYp3jEwmExaLhcLCQiNbqo6TiJRlO3fuJC0tjYiICGrXrk14eDhjxowxZiIdOHCAjIwMhg8fjpubG4GBgbz22mucPXvWmGXavn17UlJSitWL8vHxoVWrVmzYsME45ufnh7u7u7HzExTNlFq3bh3PP/+8caxSpUo3jOJfm5ASESkpU6dOZcGCBYSGhuLn5wcUPdvZEudt2rTh119/5aeffgJ+f7T86tWrJCUlERMTQ8OGDfH29mbhwoWMHTvWSN4DfPbZZ9SqVYuPPvqI9PR0IiIijJ2fJk2aRO/evXn++ed5+umnCQ4OZsGCBWzevJm2bdsW+z7bgG5FHsEXkfLl/PnzPPjgg4waNYqzZ89iNpvp1asXixcvJj8/H4CgoCD8/PyoWbOmcV5oaCi+vr7Gc+egQYPYvHlzsV3z9uzZw+nTpwkJCQGgc+fOpKamkpaWVqwNVquV1q1bGwmp6+Oovb19hU5CXa9CPaHn5eUZN/FevXoxf/58Zs+ebdQZOX/+POnp6Tz//PNcvnyZd999l1mzZlFYWGhsHx4UFMT58+c5evSo8bnt27fHbDazc+dO3N3defvtt3FwcGDcuHEEBAQQEhLC2bNn6du3LyaTCW9vb6Kjo+nUqdNN22lnZ4eDg4N+qCJS5p07d46QkBAef/xxI7H0yiuvsHjxYhYtWgQU3cBtM0Rt6tevT7t27YzkUs+ePTl+/LixQx5A1apVCQoKYvXq1caxGjVqMGHCBObPn28cs7e3p3bt2je07dqBCBGR0pCZmUliYiIRERG8/vrreHl5AfDVV1/RvXt3IiIiOHbsGNnZ2UZS6vdilouLC1WrViU5OZnXXnuNgwcPcujQIV577TVat25tDAZ8+umntG3blm7duvHpp5/SpEkTBg0axJdffomfnx9vvPEGP//8MydPnmTMmDHUqlULk8l0Q/LJZDKp8yQi5UpBQQFdu3Zl8+bNrFy5kg8++IBJkyaxePFio3xOaGgoZrOZU6dOGefVqlWLWrVqcfr0aQAiIiLIz88nKiqKt956ixdeeIF3332XyZMn4+LiAkDbtm25fPkyjRo1KtYGW8y8dpKJ4uitVagn9TVr1vD+++8zduxY3nzzTY4fPw5g1DX54YcfyMnJITAwED8/P2JjY6lRowYbNmxg/PjxAHTo0IErV64UKzjepk0b7O3t2b59O1CUuNqwYQPz58/nk08+IScnhy+++IKuXbsW+zFqDb6IlCVms7lYXPozI+M1a9akR48e/PDDD/Tt2xcfHx9GjBjBQw89xJIlS4CiadJZWVmkpqYa57m6ulK3bl1ji/GuXbtitVqLxVZHR0fCwsJ4/PHHuXLlinH8H//4B61atfrL1ysicrt5eHhQv359EhISeOihh3jwwQfJyMggKioKf39/HnvsMU6dOsWhQ4c4fPhwseT9zdSsWZOqVavSvn17Bg8ejL+/PwCnTp1i2bJlHD9+nL1795KYmMjQoUMB8PX1JTY2lqSkJPr16weAs7Mzbm5uN8zOV6dJRMqqvLw8srKygN9/RvX29iYyMpIqVaowZ84c+vTpw8yZMzly5Aj79u0Diko9ZGZmkpCQYJxntVrZsWMHHTt2xGKxUKtWLe666y569erFiRMn2LdvHxMnTuTJJ58s9n2VK1e+ZXsUU/+ccrkQ3Gw2YzKZjJGkP9qpxFZHZP78+QQFBTFu3DgA4uLiyM7OZu3atURHR+Pr64ujoyP9+/fnrbfewsXFxfiO3NxcoCgbWqVKFX788Ufj82vUqEGHDh3w9/enoKAAR0dHrFZrsZlQtpGra5fiafReREqLrTD5zerV5eTkkJ6ezl133fWnPqtdu3akpqbSuHFj49iAAQNYunQpJ0+eJCwsDEdHR1atWkWvXr0AOHv2LKtXr+bFF1/EbDbj4eFB1apVSUlJ4cqVK7i6ugJFyaquXbvetP260YtIWRcdHc3y5cuxt7encePGjBo1ihkzZuDi4sKrr76Kj48P/fv3Jzw8nMOHD5OamkrDhg1vGeMaNGhAVFQU06dP5/z580RGRpKSksJnn32G1WqlY8eO2NvbM3r0aLp162ac5+joiK+v7w2fZ6uzKiJSFm3ZsoWdO3fSqFEjXn75Ze69915mzJiB2Wy+ZU27SpUqkZWVxaOPPsqFCxfo3LkzkydPJioqisOHD1NYWEi9evXw9/fnzTffpGrVqvTu3ZsVK1bg7OxMaGioERc7duxIamoqq1atokqVKrdsp55J/5oyfxeyrWW/lr29vbGDyIkTJ/7wR2BnZ8f58+eL3ZAtFgvu7u4MGTKEXbt2kZmZSWBgIPXr1+f48eO4uroaP8Zjx44RGRlJWloarq6ueHt7c/78eS5cuGB8xxtvvMGYMWNwdHQEbj5lT7WhRKS02eKpbUmGjdVq5eOPP6Z58+bUqVOHf/3rX0yZMsWIc783ItWzZ09++eWXYrX2OnXqRGFhId9//z3VqlVj4sSJfPzxx/zrX/8iNjaWJ598kmbNmjFgwACjHV999RVz5841ElI2FovlhpmluvmLSHkQHR3N4sWLqVOnDqNHj6ZLly7s27ePoKAgfHx8jJh8//33k5WV9afqSo0ZM4aYmBiOHTvGgAEDGD9+PF5eXkycOBF/f39atmzJK6+8UmxnJ8VMESnLbDHv9OnTRimHxx57jIiICJKSkvj88885ePCgMaP+j5LpcXFxHDhwgNmzZ/Pqq68SGhqKvb29kfyHoiV8bm5u7Nu3jwEDBhAXF8cLL7xAixYtjOfOBx54gD179hjL/P5oNqv8b8rsTCnbjnM36zgtX76c6dOnc+bMGQIDA+nSpQtPPfUU1atXv+XIUqVKlfDx8TGKkNl+yIGBgWRnZ7N79266devGlClTGDBgAMHBwdx7770kJyezd+9eGjVqZCwfWbhwIVWrVr1lm6+lhwARKS22ZM6169htMSorK4sFCxZw9OhR/v3vf5ORkcHs2bOJjIzkvvvuY+/evcyfP5+LFy8yb9683/2etm3bUqlSJb7//nvq1auH1WrF1dWVtm3bsmXLFgYOHMj999+Pi4sLn332GXPmzCE0NJRp06ZRt25d43O8vb1v+vkaxReR8qphw4aYzWZ8fHyIj48nJCSEgIAAdu7cCWDE6LCwMMaPH8/PP/9MeHj478Y9e3t7IiMjjaS+p6fnDe+xWCzGrqgiImVVRkYGZrMZb29v/u///o/hw4czePBgwsLC+Oijj9i0aROdOnXi5MmT5Obm8s0331BYWHjLWVK2FVInTpygUaNGeHh4APDJJ5+QlpZGZmYmu3btokGDBoSEhLBhwwbuv/9+4uLijMkl1+rRowcXL17k0KFDNG3a9Kbvkb+uTDzpX7+eHYp3nGbMmEFkZCQnT540Mp6RkZHs2bOH8ePHk5iYyMsvv/y73+Hp6UlAQAD79u0rNpr/3XffYbVa2bVrFwDBwcFs3LiRoUOHsn37dipXrsycOXNYvXq1MZ3alpC6fhRLM6FEpCy5dtOES5cukZGRwYYNG2jfvj3Lli1j06ZNtGrVCqvVyieffMLdd9/N+PHjqVevHg0aNMDJyYkFCxZw6dKl3+3YVKlSheDgYDZt2lTseIcOHVizZo1RN6pfv34sWrSIw4cPExcXR4sWLW7r9YuIlAW1atWiZcuWRu3RXr16kZSURGpqqtHByc3NJT8/n0OHDhm7Ql/LYrFgNpuNEhZms5nq1avj6el5w06jwA1bjYuIlAXXroD6+uuv8fLy4rXXXgPgrrvuIi0tjcDAQBISEmjbtq2xy52vry+RkZGYTCa2bNkC3Lw+s61/fu+993Ly5EmGDx9OeHg4cXFxTJkyhcGDB9OkSRMAunfvzpUrVzhx4oRRfqewsND4LIvFgouLC1u3buW+++67HX8O+f+ViaTU7e442X6c9913H46OjgwdOpTvv/+e3bt3c+jQIYKCgowdoCwWC4GBgYwbN46NGzeycOFCevToYexIcu3n62YvImXZ8ePHiY6Opm7duoSEhPDBBx9Qp04dkpKSeOutt3jnnXeIiorirrvuIj4+nosXL3LvvfdSvXp1evToQaVKlZg/f/4tR6Ou1blzZ5YvX87ly5eN2Dh58mQSExONUSoABweHm3agRETuVB4eHrRu3ZrU1FTS09Pp168fDRo04IknnmDHjh1cunSJBQsW4O3tzY8//khycjJQtEzE1oGzs7MzSkEUFBRgb29vxFDtNCoi5cH69evx9vY2Vh9lZmbi7+/PsmXLSE5OxsnJiZycHEwmEzk5OXh6ehbbHa9hw4b4+vqyfv164OZJKdskkV69evHpp5/SsmVLmjZtyhtvvGHU9GvTpg1WqxVPT088PDxISEggIyPDiKU2dnZ2WK1WQkND/9SzsPzvysTd63Z3nGwdpObNm7NgwQKcnJzo3bs3Xbp0wdfXl3HjxnHw4EGg+DKR6ztOSkKJSHlhNpuZMmUKR44cYfbs2axYsYJWrVrh5+eHv78/Xbp0KVaYvGXLlsTHxxMUFMTatWtJSUnhiy++YPTo0bi5uf3h9z344IO8+OKLxeKwl5cXfn5+N7xXHSgRqWiaNGmCq6sr33zzDQDvvvsujo6ODBkyhOrVq1NQUMB7773H22+/TZs2bYCi4uS2DtZ3333HxIkTad68ubHsWTFURMqT7t274+bmxqxZswA4d+4cDzzwAPXq1SMuLo60tDRatGjBiRMnCA0N5cyZM6SkpBjnV61alcLCQmPW6a1WKdmS+YGBgcTGxvL6668TFBQE/Fbawvae6OhoBg0adMsi5ur/l4xST/nZOk7nzp1j9uzZBAQEcP78+T/sOE2YMIEXX3yRwMBA3N3d//T3dezYkbVr13L06FHjcx955BHq169PZmZmsRH967OlIiLlxbJly9iwYQNLliyhT58+xWZ6tmnThpMnT2I2m40lHiEhIezYsYPHH3+82K573333HcnJyYwYMeKG7zCbzVitVhwcHGjYsCEvvPBCiV2fiEh54u/vT7Vq1Vi5ciX3338/Xbp0ISgoiF27dtGkSZNi9fWgaDnf/PnzWbt2LQcPHsTFxYW7776b6OhowsLCSukqRET+d46OjowbN45Vq1bRr18/vL292bhxI7NmzeKNN95gxYoV1KhRg5ycHNq3b4+bmxsffPABoaGhVK5cme3bt3Pp0iWOHz9ORkYG1apVw2q1FttR+vp61ACFhYXGTqO2ZL7tv48++miJ/x3kRqWecSnpjhNAamoqzs7OHD9+nC+++ILvvvuOl19+GQ8PD20zLiJ3hL1791K/fn369OkDFCXZbcUf+/Tpw+TJk8nKyjJq5D3yyCMsW7aMfv368corr+Dj48PGjRvZunUrERERFBQU4ODgcMtNKC5fvoybm9vvFp8UEamoatWqxYgRI6hcubJxzN3dnW7dugG/lZqwWq3GrtH79++nc+fOvPnmmwQGBuLi4lIqbRcR+bsMGTKEvXv3MmPGDKZOncqPP/5Ix44d2bZtGxs3buTYsWP06dOHqlWrEhMTw8CBA4mIiMDR0ZFLly4xYcIE4uLiSExMpH///saGDrYk09mzZ4mPj+enn35i5MiRBAYG6rm0HCj1eb+36jgB9OnTh6SkJLKysoxE0SOPPELNmjXp168f8fHx/PDDD0ydOpUpU6aQnZ1NQUGBsezOdoO3t7cv9mO8cOECTzzxBC1atCA2NpaoqCjCw8ON7xcRKe/8/f2NdfjX1iQB6Nq1K+np6cbW41A0Jfrjjz8mJCSEWbNm0atXL2NHkmHDhuHo6GjMHjWZTKSlpbFo0SL69u1LnTp1mDt3LoBu/CIiN+Hi4sKoUaN46KGHih23Pate37Hy8/Nj6dKlvPrqq7Rv314JKRG5I9StW5eRI0eydu1aPvvsM5o1a0ZGRgaRkZFkZ2dz5MgR472dO3dm8+bN3H333fj7+/P+++8TFBREYWEh+fn5AOTk5LBmzRoeeeQR6tWrR0BAAO+++y5VqlShZs2apXWZ8l8q9d6Dv78/K1euBDBG4G/WcbrnnnuA3zpOb775JrNmzeLQoUM0b96cESNGMGTIEGMXE1vHKC0tjXXr1hEfH8/evXsZP348EyZMICYmhgYNGtxy/aiISHnWpUsXxo8fz7Zt24ot9Th27BjVq1cnICCA9evXG7HVarXSpEkTYmNjOXPmDHXq1LnhM3fv3s3s2bP54YcfSE9Pp2HDhnTt2pWnn36aDh06lNi1iYiUV7ZnXRsNhopIRWK1WgkLC6N///7ExMTQr18/8vLy8PHxoX///iQmJpKRkWG8v02bNrRu3dqIlRMnTsRsNhMaGkpOTg6tWrUCIDQ0lJkzZxIWFkbt2rVL5drkf2ey2oZoSsmBAwdo3bo133777U07Tvfccw8DBw5k6tSpAMbyOqvV+l93nHr37k2HDh2KTZ0WEblT9ezZk4yMDJ588kkiIiLYv38/q1atIjIykri4ONLT041BgZu5dumzxWJh7ty5HDhwgN69e+umLyIiIiL/FVtffsuWLXTv3p3GjRtz8OBB7O3tuXz5Mnl5eXh5eRnvKygoYNKkSWRnZ/Ptt99SWFjIzJkzGTRoEACnT5+mdu3a2vihnCv1pBSo4yQicjukpKTw1ltvsXXrVtLS0igsLOShhx7i+eefp0aNGsbMUhERERGRkpKfn8/WrVvx9/enUaNGxV67dhKKyWTi9ddfJzk5mbCwMCIiIvD09CydRsttUyaSUuo4iYjcPnv37sXZ2ZmmTZsWO66NHUREREREpDSViaSUjTpOIiJ/r+vj57W7mYqIiIiIlBb18wXKUFJKHScRkdtHN30RERERESlrykxSykYdJxERERERERGRO1+ZK1OvhJSIiIiIiIiIyJ2vzCWlRERERERERETkzqeklIiIiIiIiIiIlDglpUREREREREREpMQpKSUiIiIiIiIiIiVOSSkRERERERERESlxSkqJiIiIiIiIiEiJU1JKRERERERERERKnJJSIiIiIiIiIiJS4pSUEhERERERERGREqeklIiIiIiIiIiIlLj/B34oC0LyDUxEAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "palette = sns.color_palette(\"colorblind\")\n", "\n", @@ -889,7 +3333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.9" + "version": "3.12.3" }, "orig_nbformat": 4 }, From b9569e66821be95134422948d676c1fa660118e7 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 16 Jan 2026 11:12:59 +0000 Subject: [PATCH 12/16] fix indices for plpr model section --- doc/guide/models/plm/lplr.rst | 2 +- doc/guide/models/plm/plpr.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/guide/models/plm/lplr.rst b/doc/guide/models/plm/lplr.rst index 76073e99..6e3a00cd 100644 --- a/doc/guide/models/plm/lplr.rst +++ b/doc/guide/models/plm/lplr.rst @@ -9,5 +9,5 @@ The high-dimensional vector :math:`X = (X_1, \ldots, X_p)` consists of confoundi :math:`\text{expit}` is the logistic link function .. math:: - \text{expit} ( X ) = \frac{1}{1 + e^{-x}} + \text{expit} ( X ) = \frac{1}{1 + e^{-x}}. diff --git a/doc/guide/models/plm/plpr.rst b/doc/guide/models/plm/plpr.rst index 7049c086..11cb2f38 100644 --- a/doc/guide/models/plm/plpr.rst +++ b/doc/guide/models/plm/plpr.rst @@ -1,6 +1,6 @@ Suppose a panel study observes each of :math:`N` individuals over :math:`T` time periods (or waves). For each individual :math:`i=1,\dots,N` and each period :math:`t=1,\dots,T`, the data consists of the -triple :math:`(Y_it, D_it, X_it)`. Let :math:`\{(Y_it, D_it, X_it) : t = 1, \dots , T\}_{i=1}^N` +triple :math:`(Y_{it}, D_{it}, X_{it})`. Let :math:`\{(Y_{it}, D_{it}, X_{it}) : t = 1, \dots , T\}_{i=1}^N` denote :math:`N` independent and identically distributed (iid) random vectors, where each vector corresponds to individual :math:`i` observed across all T waves. From 81205d723484afaf6bb334fe9b895e840ac09519 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 16 Jan 2026 13:00:24 +0000 Subject: [PATCH 13/16] remove alpha from lassocv --- doc/examples/py_double_ml_plpr.ipynb | 334 +++++++++++++-------------- 1 file changed, 167 insertions(+), 167 deletions(-) diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb index 25a1d8fe..4b337b15 100644 --- a/doc/examples/py_double_ml_plpr.ipynb +++ b/doc/examples/py_double_ml_plpr.ipynb @@ -1611,13 +1611,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('columntransformer',\n",
+       "
Pipeline(steps=[('columntransformer',\n",
        "                 ColumnTransformer(remainder='passthrough',\n",
        "                                   transformers=[('poly_x', PolyPlus(),\n",
        "                                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
@@ -2046,7 +2046,7 @@
        "                                                   51, 52, 53, 54, 55, 56, 57,\n",
        "                                                   58, 59])])),\n",
        "                ('standardscaler', StandardScaler()),\n",
-       "                ('lassocv', LassoCV(cv=2, n_alphas=20, n_jobs=5))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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PolyPlus()
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PolyPlus()
 
passthrough
 
StandardScaler()
 
LassoCV(cv=2, n_jobs=5)
 
"
       ],
       "text/plain": [
        "Pipeline(steps=[('columntransformer',\n",
@@ -2085,17 +2085,17 @@
        "                                                   51, 52, 53, 54, 55, 56, 57,\n",
        "                                                   58, 59])])),\n",
        "                ('standardscaler', StandardScaler()),\n",
-       "                ('lassocv', LassoCV(cv=2, n_alphas=20, n_jobs=5))])"
+       "                ('lassocv', LassoCV(cv=2, n_jobs=5))])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "ml_lasso = make_pipeline(\n",
-    "    preprocessor, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n",
+    "    preprocessor, StandardScaler(), LassoCV(cv=2, n_jobs=5)\n",
     ")\n",
     "\n",
     "ml_lasso"
@@ -2103,7 +2103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2111,7 +2111,7 @@
      "output_type": "stream",
      "text": [
       "            coef   std err          t          P>|t|     2.5 %    97.5 %\n",
-      "d_diff  0.516443  0.018066  28.585995  1.003247e-179  0.481034  0.551852\n"
+      "d_diff  0.517696  0.018044  28.690074  5.074285e-181  0.482329  0.553062\n"
      ]
     }
    ],
@@ -2130,7 +2130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2139,7 +2139,7 @@
        "1050"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2157,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2165,7 +2165,7 @@
      "output_type": "stream",
      "text": [
       "       coef   std err          t         P>|t|     2.5 %    97.5 %\n",
-      "d  0.569761  0.028406  20.057831  1.724430e-89  0.514086  0.625436\n"
+      "d  0.560037  0.028292  19.794714  3.306228e-87  0.504585  0.615488\n"
      ]
     },
     {
@@ -2174,7 +2174,7 @@
        "1051"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2211,13 +2211,13 @@
     ")\n",
     "\n",
     "ml_lasso_wg = make_pipeline(\n",
-    "    preprocessor_wg, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n",
+    "    preprocessor_wg, StandardScaler(), LassoCV(cv=2, n_jobs=5)\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2225,7 +2225,7 @@
      "output_type": "stream",
      "text": [
       "              coef   std err          t  P>|t|     2.5 %    97.5 %\n",
-      "d_demean  1.152402  0.014064  81.940739    0.0  1.124837  1.179966\n"
+      "d_demean  1.151822  0.014172  81.277037    0.0  1.124047  1.179598\n"
      ]
     },
     {
@@ -2234,7 +2234,7 @@
        "525"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2262,7 +2262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2271,7 +2271,7 @@
        "39"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2293,7 +2293,7 @@
     "    remainder=\"passthrough\",\n",
     ")\n",
     "ml_lasso_alt = make_pipeline(\n",
-    "    preprocessor_alt, StandardScaler(), LassoCV(n_alphas=20, cv=2, n_jobs=5)\n",
+    "    preprocessor_alt, StandardScaler(), LassoCV(cv=2, n_jobs=5)\n",
     ")\n",
     "\n",
     "plpr_lasso_wg.learner[\"ml_l\"] = ml_lasso_alt\n",
@@ -2305,13 +2305,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
ColumnTransformer(remainder='passthrough',\n",
-       "                  transformers=[('poly_x', PolyPlus(), [2, 5, 21])])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
+       "
ColumnTransformer(remainder='passthrough',\n",
+       "                  transformers=[('poly_x', PolyPlus(), [2, 5, 21])])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
       ],
       "text/plain": [
        "ColumnTransformer(remainder='passthrough',\n",
        "                  transformers=[('poly_x', PolyPlus(), [2, 5, 21])])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2755,7 +2755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2775,7 +2775,7 @@
        "       'remainder__x28', 'remainder__x29'], dtype=object)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2797,7 +2797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2812,7 +2812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2840,13 +2840,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb8bee93245c430fb5fce83c2c66eaf2",
+       "model_id": "0e715d42cedf4f24b082a3742b6ba7a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2860,7 +2860,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b7481e53a7b44e1594db616795c3eb2a",
+       "model_id": "c57d57e08ecf42fb9cd962f8743efa55",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2903,23 +2903,23 @@
        "  \n",
        "    \n",
        "      d\n",
-       "      0.50256\n",
-       "      0.008025\n",
-       "      62.620517\n",
+       "      0.495091\n",
+       "      0.007491\n",
+       "      66.088483\n",
        "      0.0\n",
-       "      0.48683\n",
-       "      0.518289\n",
+       "      0.480408\n",
+       "      0.509774\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "      coef   std err          t  P>|t|    2.5 %    97.5 %\n",
-       "d  0.50256  0.008025  62.620517    0.0  0.48683  0.518289"
+       "       coef   std err          t  P>|t|     2.5 %    97.5 %\n",
+       "d  0.495091  0.007491  66.088483    0.0  0.480408  0.509774"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2945,13 +2945,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "508dec2d1acd4dfe81fc4d2758acaaef",
+       "model_id": "c36de95d4af446f29d9f84c53133e9ed",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2965,7 +2965,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b5e203b1b234aeaba6bb579b6819aa8",
+       "model_id": "cac1a8fb1bf34487b59b1551fb614da5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3008,12 +3008,12 @@
        "  \n",
        "    \n",
        "      d\n",
-       "      0.461759\n",
-       "      0.010308\n",
-       "      44.796854\n",
+       "      0.489759\n",
+       "      0.010002\n",
+       "      48.968271\n",
        "      0.0\n",
-       "      0.441556\n",
-       "      0.481962\n",
+       "      0.470156\n",
+       "      0.509362\n",
        "    \n",
        "  \n",
        "\n",
@@ -3021,10 +3021,10 @@
       ],
       "text/plain": [
        "       coef   std err          t  P>|t|     2.5 %    97.5 %\n",
-       "d  0.461759  0.010308  44.796854    0.0  0.441556  0.481962"
+       "d  0.489759  0.010002  48.968271    0.0  0.470156  0.509362"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3043,13 +3043,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1acbbcfc44ee4b9b8c995f6e6620f788",
+       "model_id": "39ffcc1fef014cdc8010286cf9fe469c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3063,7 +3063,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1fb9f3dfeb9748c896145a2d274c154f",
+       "model_id": "860738384eec45e5893f926360202fa3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3106,23 +3106,23 @@
        "  \n",
        "    \n",
        "      d_diff\n",
-       "      0.545698\n",
-       "      0.008928\n",
-       "      61.12373\n",
+       "      0.549295\n",
+       "      0.008736\n",
+       "      62.878843\n",
        "      0.0\n",
-       "      0.5282\n",
-       "      0.563196\n",
+       "      0.532174\n",
+       "      0.566417\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "            coef   std err         t  P>|t|   2.5 %    97.5 %\n",
-       "d_diff  0.545698  0.008928  61.12373    0.0  0.5282  0.563196"
+       "            coef   std err          t  P>|t|     2.5 %    97.5 %\n",
+       "d_diff  0.549295  0.008736  62.878843    0.0  0.532174  0.566417"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3141,13 +3141,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "73577cb5b19b428dab75d207746df8db",
+       "model_id": "072390e43ee2426caf19fa0ffbb35cc4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3161,7 +3161,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e93e0151b1504b49bcd4a77366f421d9",
+       "model_id": "bbb6ac2639ac4a91957b849b4cd874f3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3204,23 +3204,23 @@
        "  \n",
        "    \n",
        "      d_demean\n",
-       "      1.1365\n",
-       "      0.005177\n",
-       "      219.547846\n",
+       "      1.133962\n",
+       "      0.005109\n",
+       "      221.970892\n",
        "      0.0\n",
-       "      1.126354\n",
-       "      1.146646\n",
+       "      1.123949\n",
+       "      1.143975\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "            coef   std err           t  P>|t|     2.5 %    97.5 %\n",
-       "d_demean  1.1365  0.005177  219.547846    0.0  1.126354  1.146646"
+       "              coef   std err           t  P>|t|     2.5 %    97.5 %\n",
+       "d_demean  1.133962  0.005109  221.970892    0.0  1.123949  1.143975"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3239,7 +3239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3249,15 +3249,15 @@
       "True treatment effect: 0.5\n",
       "\n",
       "      Model    theta       se  ci_lower  ci_upper\n",
-      "cre_general 0.502560 0.008025  0.486830  0.518289\n",
-      " cre_normal 0.461759 0.010308  0.441556  0.481962\n",
-      "   fd_exact 0.545698 0.008928  0.528200  0.563196\n",
-      "  wg_approx 1.136500 0.005177  1.126354  1.146646\n"
+      "cre_general 0.495091 0.007491  0.480408  0.509774\n",
+      " cre_normal 0.489759 0.010002  0.470156  0.509362\n",
+      "   fd_exact 0.549295 0.008736  0.532174  0.566417\n",
+      "  wg_approx 1.133962 0.005109  1.123949  1.143975\n"
      ]
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
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       ]

From 82c7f828e4d3a6460771bc640433fec151665b03 Mon Sep 17 00:00:00 2001
From: SvenKlaassen 
Date: Mon, 19 Jan 2026 08:20:38 +0000
Subject: [PATCH 14/16] update release notes

---
 doc/release/release.rst | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/doc/release/release.rst b/doc/release/release.rst
index ccfa60f4..b8be9a8e 100644
--- a/doc/release/release.rst
+++ b/doc/release/release.rst
@@ -7,6 +7,37 @@ Release Notes
 
   .. tab-item:: Python
 
+    .. dropdown:: DoubleML 0.11.2
+      :class-title: sd-bg-primary sd-font-weight-bold
+      :open:
+
+      - **Release highlight:** DoubleML for static panel models with fixed effects (by `Julian Diefenbacher `_)
+
+        - Implementation via ``DoubleMLPLPR`` class
+          `Py #378 `_
+        - Extended User Guide and Example Gallery
+          `Docs #267 `_
+
+      - Refactoring of the ``DoubleMLFramework`` class
+        `Py #376 `_
+
+      - Added tuning to workflow
+        `Docs #268 `_
+        `Docs #269 `_
+
+      - Maintenance package
+        `Py #380 `_
+        `Py #381 `_
+        `Py #382 `_
+        `Py #384 `_
+
+      - Maintenance documentation
+        `Docs #263 `_
+        `Docs #264 `_
+        `Docs #265 `_
+        `Docs #266 `_
+        `Docs #270 `_
+
     .. dropdown:: DoubleML 0.11.1
       :class-title: sd-bg-primary sd-font-weight-bold
       :open:

From f63b175c478459b790e64b812a6e288f6098c455 Mon Sep 17 00:00:00 2001
From: SvenKlaassen 
Date: Mon, 19 Jan 2026 08:24:07 +0000
Subject: [PATCH 15/16] add pr for release note updates

---
 doc/release/release.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/release/release.rst b/doc/release/release.rst
index b8be9a8e..6890e5ea 100644
--- a/doc/release/release.rst
+++ b/doc/release/release.rst
@@ -37,6 +37,8 @@ Release Notes
         `Docs #265 `_
         `Docs #266 `_
         `Docs #270 `_
+        `Docs #272 `_
+        `Docs #273 `_
 
     .. dropdown:: DoubleML 0.11.1
       :class-title: sd-bg-primary sd-font-weight-bold

From c59c3832e45eea8d4e27b5ec7d9658d95a01edfc Mon Sep 17 00:00:00 2001
From: SvenKlaassen 
Date: Mon, 19 Jan 2026 09:01:44 +0000
Subject: [PATCH 16/16] fix typos in plpr example nb

---
 doc/examples/py_double_ml_plpr.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/examples/py_double_ml_plpr.ipynb b/doc/examples/py_double_ml_plpr.ipynb
index 4b337b15..17616ee2 100644
--- a/doc/examples/py_double_ml_plpr.ipynb
+++ b/doc/examples/py_double_ml_plpr.ipynb
@@ -1245,7 +1245,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that the outcome, treatment and covariate variables are now labeled `y_deman`, `d_deman`, `xi_deman` to indicate the within-group transformations."
+    "We see that the outcome, treatment and covariate variables are now labeled `y_demean`, `d_demean`, `xi_demean` to indicate the within-group transformations."
    ]
   },
   {