From a54647bae80dc00fd044741c8de2855d6483a143 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 5 Dec 2025 16:01:26 +0100 Subject: [PATCH 1/5] first plpr version --- monte-cover/src/montecover/plm/__init__.py | 2 + monte-cover/src/montecover/plm/plpr_ate.py | 137 +++++++++++++++++++++ results/plm/plpr_ate_config.yml | 59 +++++++++ results/plm/plpr_ate_coverage.csv | 113 +++++++++++++++++ results/plm/plpr_ate_metadata.csv | 2 + scripts/plm/plpr_ate.py | 13 ++ scripts/plm/plpr_ate_config.yml | 57 +++++++++ 7 files changed, 383 insertions(+) create mode 100644 monte-cover/src/montecover/plm/plpr_ate.py create mode 100644 results/plm/plpr_ate_config.yml create mode 100644 results/plm/plpr_ate_coverage.csv create mode 100644 results/plm/plpr_ate_metadata.csv create mode 100644 scripts/plm/plpr_ate.py create mode 100644 scripts/plm/plpr_ate_config.yml diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py index 0edaedc..493551d 100644 --- a/monte-cover/src/montecover/plm/__init__.py +++ b/monte-cover/src/montecover/plm/__init__.py @@ -3,6 +3,7 @@ from montecover.plm.lplr_ate import LPLRATECoverageSimulation from montecover.plm.lplr_ate_tune import LPLRATETuningCoverageSimulation from montecover.plm.pliv_late import PLIVLATECoverageSimulation +from montecover.plm.plpr_ate import PLPRATECoverageSimulation from montecover.plm.plr_ate import PLRATECoverageSimulation from montecover.plm.plr_ate_sensitivity import PLRATESensitivityCoverageSimulation from montecover.plm.plr_ate_tune import PLRATETuningCoverageSimulation @@ -18,4 +19,5 @@ "PLRATETuningCoverageSimulation", "LPLRATECoverageSimulation", "LPLRATETuningCoverageSimulation", + "PLPRATECoverageSimulation", ] diff --git a/monte-cover/src/montecover/plm/plpr_ate.py b/monte-cover/src/montecover/plm/plpr_ate.py new file mode 100644 index 0000000..e5f6a50 --- /dev/null +++ b/monte-cover/src/montecover/plm/plpr_ate.py @@ -0,0 +1,137 @@ +from typing import Any, Dict, Optional + +import doubleml as dml +from doubleml.plm.datasets import make_plpr_CP2025 + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class PLPRATECoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + self.oracle_values["theta"] = self.dgp_parameters["theta"] + + def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + score = dml_params["score"] + approach = dml_params["approach"] + + # Model + dml_model = dml.DoubleMLPLPR( + obj_dml_data=dml_data, + ml_l=ml_g, + ml_m=ml_m, + ml_g=ml_g if score == "IV-type" else None, + score=score, + approach=approach, + ) + dml_model.fit() + + result = { + "coverage": [], + } + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=dml_model.coef, + oracle_thetas=self.oracle_values["theta"], + confint=dml_model.confint(level=level), + joint_confint=None, + ) + + # add parameters to the result + for res in level_result.values(): + res.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "Score": score, + "Approach": approach, + "level": level, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner g", "Learner m", "Score", "Approach", "level"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: + """Generate data for the simulation.""" + data = make_plpr_CP2025( + num_id=dgp_params["num_id"], + num_t=dgp_params["num_t"], + dim_x=dgp_params["dim_x"], + theta=dgp_params["theta"], + dgp_type=dgp_params["dgp_type"], + ) + dml_data = dml.DoubleMLPanelData( + data, + y_col="y", + d_cols="d", + t_col="time", + id_col="id", + static_panel=True, + ) + return dml_data diff --git a/results/plm/plpr_ate_config.yml b/results/plm/plpr_ate_config.yml new file mode 100644 index 0000000..5058848 --- /dev/null +++ b/results/plm/plpr_ate_config.yml @@ -0,0 +1,59 @@ +simulation_parameters: + repetitions: 100 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + theta: + - 0.5 + dim_x: + - 5 + num_id: + - 250 + num_t: + - 10 + dgp_type: + - dgp1 +learner_definitions: + lasso: &id001 + name: LassoCV + rf: &id002 + name: RF Regr. + params: + n_estimators: 200 + max_features: 10 + max_depth: 5 + min_samples_leaf: 20 + lgbm: &id003 + name: LGBM Regr. + params: + n_estimators: 500 + learning_rate: 0.01 +dml_parameters: + learners: + - ml_g: *id001 + ml_m: *id001 + - ml_g: *id002 + ml_m: *id002 + - ml_g: *id001 + ml_m: *id002 + - ml_g: *id002 + ml_m: *id001 + - ml_g: *id003 + ml_m: *id003 + - ml_g: *id003 + ml_m: *id001 + - ml_g: *id001 + ml_m: *id003 + score: + - partialling out + - IV-type + approach: + - cre_general + - cre_normal + - fd_exact + - wg_approx +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/plm/plpr_ate_coverage.csv b/results/plm/plpr_ate_coverage.csv new file mode 100644 index 0000000..350b61d --- /dev/null +++ b/results/plm/plpr_ate_coverage.csv @@ -0,0 +1,113 @@ +Learner g,Learner m,Score,Approach,level,Coverage,CI 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out,wg_approx,0.95,0.22,0.08587371049560737,0.05947757489790515,100 diff --git a/results/plm/plpr_ate_metadata.csv b/results/plm/plpr_ate_metadata.csv new file mode 100644 index 0000000..3151396 --- /dev/null +++ b/results/plm/plpr_ate_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.12.dev0,PLPRATECoverageSimulation,2025-12-05 16:00,24.24196228583654,3.12.9,scripts/plm/plpr_ate_config.yml diff --git a/scripts/plm/plpr_ate.py b/scripts/plm/plpr_ate.py new file mode 100644 index 0000000..a63f90b --- /dev/null +++ b/scripts/plm/plpr_ate.py @@ -0,0 +1,13 @@ +from montecover.plm import PLPRATECoverageSimulation + +# Create and run simulation with config file +sim = PLPRATECoverageSimulation( + config_file="scripts/plm/plpr_ate_config.yml", + log_level="INFO", + log_file="logs/plm/plpr_ate_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/plm/", file_prefix="plpr_ate") + +# Save config file for reproducibility +sim.save_config("results/plm/plpr_ate_config.yml") diff --git a/scripts/plm/plpr_ate_config.yml b/scripts/plm/plpr_ate_config.yml new file mode 100644 index 0000000..2b4ba35 --- /dev/null +++ b/scripts/plm/plpr_ate_config.yml @@ -0,0 +1,57 @@ +# Simulation parameters for PLR ATE Coverage + +simulation_parameters: + repetitions: 100 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + theta: [0.5] # Treatment effect + dim_x: [5] # Number of covariates + num_id: [250] # number of units + num_t: [10] # number of time periods + dgp_type: ["dgp1"] + + +# Define reusable learner configurations +learner_definitions: + lasso: &lasso + name: "LassoCV" + + rf: &rf + name: "RF Regr." + params: + n_estimators: 200 + max_features: 10 + max_depth: 5 + min_samples_leaf: 20 + + lgbm: &lgbm + name: "LGBM Regr." + params: + n_estimators: 500 + learning_rate: 0.01 + +dml_parameters: + learners: + - ml_g: *lasso + ml_m: *lasso + - ml_g: *rf + ml_m: *rf + - ml_g: *lasso + ml_m: *rf + - ml_g: *rf + ml_m: *lasso + - ml_g: *lgbm + ml_m: *lgbm + - ml_g: *lgbm + ml_m: *lasso + - ml_g: *lasso + ml_m: *lgbm + + score: ["partialling out", "IV-type"] + approach: ["cre_general", "cre_normal", "fd_exact", "wg_approx"] + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From be99f52c49c1b38222c8cda9ea4aef55baf2a9a5 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 8 Dec 2025 14:52:33 +0100 Subject: [PATCH 2/5] add plpr results --- doc/_quarto-dev.yml | 3 +- doc/_website.yml | 1 + doc/plm/plpr.qmd | 185 +++++++++++++++ monte-cover/src/montecover/plm/__init__.py | 3 + monte-cover/src/montecover/plm/plpr_ate.py | 11 +- .../src/montecover/plm/plpr_ate_tune.py | 168 ++++++++++++++ monte-cover/src/montecover/plm/plr_ate.py | 5 + .../src/montecover/plm/plr_ate_tune.py | 3 +- results/plm/plpr_ate_config.yml | 30 +-- results/plm/plpr_ate_coverage.csv | 210 ++++++++---------- results/plm/plpr_ate_metadata.csv | 2 +- results/plm/plpr_ate_tune_config.yml | 36 +++ results/plm/plpr_ate_tune_coverage.csv | 49 ++++ results/plm/plpr_ate_tune_metadata.csv | 2 + scripts/plm/plpr_ate_config.yml | 28 +-- scripts/plm/plpr_ate_tune.py | 13 ++ scripts/plm/plpr_ate_tune_config.yml | 30 +++ 17 files changed, 610 insertions(+), 169 deletions(-) create mode 100644 doc/plm/plpr.qmd create mode 100644 monte-cover/src/montecover/plm/plpr_ate_tune.py create mode 100644 results/plm/plpr_ate_tune_config.yml create mode 100644 results/plm/plpr_ate_tune_coverage.csv create mode 100644 results/plm/plpr_ate_tune_metadata.csv create mode 100644 scripts/plm/plpr_ate_tune.py create mode 100644 scripts/plm/plpr_ate_tune_config.yml diff --git a/doc/_quarto-dev.yml b/doc/_quarto-dev.yml index b73319b..8a5d749 100644 --- a/doc/_quarto-dev.yml +++ b/doc/_quarto-dev.yml @@ -20,8 +20,9 @@ website: - plm/plr.qmd - plm/plr_gate.qmd - plm/plr_cate.qmd - - plm/pliv.qmd - plm/lplr.qmd + - plm/plpr.qmd + - plm/pliv.qmd # DID - did/did_pa.qmd - did/did_cs.qmd diff --git a/doc/_website.yml b/doc/_website.yml index b497b60..a6373d5 100644 --- a/doc/_website.yml +++ b/doc/_website.yml @@ -25,6 +25,7 @@ website: - plm/plr_gate.qmd - plm/plr_cate.qmd - plm/lplr.qmd + - plm/plpr.qmd - plm/pliv.qmd - text: "DID" menu: diff --git a/doc/plm/plpr.qmd b/doc/plm/plpr.qmd new file mode 100644 index 0000000..3bfbc61 --- /dev/null +++ b/doc/plm/plpr.qmd @@ -0,0 +1,185 @@ +--- +title: "PLPR Models" + +jupyter: python3 +--- + + +```{python} +#| echo: false + +import numpy as np +import pandas as pd +from itables import init_notebook_mode +import os +import sys + +doc_dir = os.path.abspath(os.path.join(os.getcwd(), "..")) +if doc_dir not in sys.path: + sys.path.append(doc_dir) + +from utils.style_tables import generate_and_show_styled_table + +init_notebook_mode(all_interactive=True) +``` + +## Coverage + +The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered: + + - DGP 1: Linear in the nuisance parameters + - DGP 2: Non-linear and smooth in the nuisance parameters + - DGP 3: Non-linear and discontinuous in the nuisance parameters + + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/plm/plpr_ate_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data and rename columns +df_coverage = pd.read_csv("../../results/plm/plpr_ate_coverage.csv", index_col=None) + +if "repetition" in df_coverage.columns and df_coverage["repetition"].nunique() == 1: + n_rep_coverage = df_coverage["repetition"].unique()[0] +elif "n_rep" in df_coverage.columns and df_coverage["n_rep"].nunique() == 1: + n_rep_coverage = df_coverage["n_rep"].unique()[0] +else: + n_rep_coverage = "N/A" # Fallback if n_rep cannot be determined + +display_columns_coverage = ["Learner g", "Learner m", "DGP", "Approach", "Bias", "CI Length", "Coverage", "Loss g", "Loss m"] +``` + +### Partialling out + +```{python} +# | echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.95, "Score": "partialling out"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.9, "Score": "partialling out"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +### IV-type + +For the IV-type score, the learners `ml_l` and `ml_g` are both set to the same type of learner (here **Learner g**). + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.95, "Score": "IV-type"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.9, "Score": "IV-type"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` + + +## Tuning + +The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered: + + - DGP 1: Linear in the nuisance parameters + - DGP 2: Non-linear and smooth in the nuisance parameters + - DGP 3: Non-linear and discontinuous in the nuisance parameters + +This is only an example as the untuned version just relies on the default configuration. + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/plm/plpr_ate_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data +df_tune_cov = pd.read_csv("../../results/plm/plpr_ate_tune_coverage.csv", index_col=None) + +assert df_tune_cov["repetition"].nunique() == 1 +n_rep_tune_cov = df_tune_cov["repetition"].unique()[0] + +display_columns_tune_cov = ["Learner g", "Learner m", "Tuned", "DGP", "Approach", "Bias", "CI Length", "Coverage", "Loss g", "Loss m"] +``` + + +### Partialling out + +```{python} +# | echo: false + +generate_and_show_styled_table( + main_df=df_tune_cov, + filters={"level": 0.95, "Score": "partialling out"}, + display_cols=display_columns_tune_cov, + n_rep=n_rep_tune_cov, + level_col="level", + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_tune_cov, + filters={"level": 0.9, "Score": "partialling out"}, + display_cols=display_columns_tune_cov, + n_rep=n_rep_tune_cov, + level_col="level", + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, + coverage_highlight_cols=["Coverage"] +) +``` diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py index 493551d..4be0ccb 100644 --- a/monte-cover/src/montecover/plm/__init__.py +++ b/monte-cover/src/montecover/plm/__init__.py @@ -4,6 +4,7 @@ from montecover.plm.lplr_ate_tune import LPLRATETuningCoverageSimulation from montecover.plm.pliv_late import PLIVLATECoverageSimulation from montecover.plm.plpr_ate import PLPRATECoverageSimulation +from montecover.plm.plpr_ate_tune import PLPRATETuningCoverageSimulation from montecover.plm.plr_ate import PLRATECoverageSimulation from montecover.plm.plr_ate_sensitivity import PLRATESensitivityCoverageSimulation from montecover.plm.plr_ate_tune import PLRATETuningCoverageSimulation @@ -17,6 +18,8 @@ "PLRCATECoverageSimulation", "PLRATESensitivityCoverageSimulation", "PLRATETuningCoverageSimulation", + "PLPRATECoverageSimulation", + "PLPRATETuningCoverageSimulation", "LPLRATECoverageSimulation", "LPLRATETuningCoverageSimulation", "PLPRATECoverageSimulation", diff --git a/monte-cover/src/montecover/plm/plpr_ate.py b/monte-cover/src/montecover/plm/plpr_ate.py index e5f6a50..cf3516f 100644 --- a/monte-cover/src/montecover/plm/plpr_ate.py +++ b/monte-cover/src/montecover/plm/plpr_ate.py @@ -8,7 +8,7 @@ class PLPRATECoverageSimulation(BaseSimulation): - """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" + """Simulation class for coverage properties of DoubleMLPLPR for ATE estimation.""" def __init__( self, @@ -65,6 +65,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: approach=approach, ) dml_model.fit() + nuisance_loss = dml_model.nuisance_loss result = { "coverage": [], @@ -87,6 +88,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Score": score, "Approach": approach, "level": level, + "Loss g": nuisance_loss["ml_l"].mean() if score == "partialling out" else nuisance_loss["ml_g"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -99,11 +102,13 @@ def summarize_results(self): self.logger.info("Summarizing simulation results") # Group by parameter combinations - groupby_cols = ["Learner g", "Learner m", "Score", "Approach", "level"] + groupby_cols = ["Learner g", "Learner m", "Score", "Approach", "DGP", "level"] aggregation_dict = { "Coverage": "mean", "CI Length": "mean", "Bias": "mean", + "Loss g": "mean", + "Loss m": "mean", "repetition": "count", } @@ -124,7 +129,7 @@ def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: num_t=dgp_params["num_t"], dim_x=dgp_params["dim_x"], theta=dgp_params["theta"], - dgp_type=dgp_params["dgp_type"], + dgp_type=dgp_params["DGP"], ) dml_data = dml.DoubleMLPanelData( data, diff --git a/monte-cover/src/montecover/plm/plpr_ate_tune.py b/monte-cover/src/montecover/plm/plpr_ate_tune.py new file mode 100644 index 0000000..041a284 --- /dev/null +++ b/monte-cover/src/montecover/plm/plpr_ate_tune.py @@ -0,0 +1,168 @@ +from typing import Any, Dict, Optional + +import doubleml as dml +import optuna +from doubleml.plm.datasets import make_plpr_CP2025 + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params + + +class PLPRATETuningCoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLPLPR for ATE estimation with tuning.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + # tuning specific settings + self._param_space = {"ml_l": lgbm_reg_params, "ml_m": lgbm_reg_params} + + self._optuna_settings = { + "n_trials": 50, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + self.oracle_values["theta"] = self.dgp_parameters["theta"] + + def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + score = dml_params["score"] + approach = dml_params["approach"] + + # Model + dml_model = dml.DoubleMLPLPR( + obj_dml_data=dml_data, + ml_l=ml_g, + ml_m=ml_m, + ml_g=ml_g if score == "IV-type" else None, + score=score, + approach=approach, + ) + + dml_model_tuned = dml.DoubleMLPLPR( + obj_dml_data=dml_data, + ml_l=ml_g, + ml_m=ml_m, + ml_g=ml_g if score == "IV-type" else None, + score=score, + approach=approach, + ) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + + result = { + "coverage": [], + } + for model in [dml_model, dml_model_tuned]: + model.fit() + nuisance_loss = model.nuisance_loss + + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=dml_model.coef, + oracle_thetas=self.oracle_values["theta"], + confint=dml_model.confint(level=level), + joint_confint=None, + ) + + # add parameters to the result + for res in level_result.values(): + res.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "Score": score, + "Approach": approach, + "level": level, + "Tuned": model is dml_model_tuned, + "Loss g": nuisance_loss["ml_l"].mean() if score == "partialling out" else nuisance_loss["ml_g"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner g", "Learner m", "Score", "Approach", "DGP", "level", "Tuned"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "Loss g": "mean", + "Loss m": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: + """Generate data for the simulation.""" + data = make_plpr_CP2025( + num_id=dgp_params["num_id"], + num_t=dgp_params["num_t"], + dim_x=dgp_params["dim_x"], + theta=dgp_params["theta"], + dgp_type=dgp_params["DGP"], + ) + dml_data = dml.DoubleMLPanelData( + data, + y_col="y", + d_cols="d", + t_col="time", + id_col="id", + static_panel=True, + ) + return dml_data diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py index 0f19214..dc56746 100644 --- a/monte-cover/src/montecover/plm/plr_ate.py +++ b/monte-cover/src/montecover/plm/plr_ate.py @@ -63,6 +63,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: score=score, ) dml_model.fit() + nuisance_loss = dml_model.nuisance_loss result = { "coverage": [], @@ -84,6 +85,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Learner m": learner_m_name, "Score": score, "level": level, + "Loss g": nuisance_loss["ml_l"].mean() if score == "partialling out" else nuisance_loss["ml_g"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -101,6 +104,8 @@ def summarize_results(self): "Coverage": "mean", "CI Length": "mean", "Bias": "mean", + "Loss g": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/monte-cover/src/montecover/plm/plr_ate_tune.py b/monte-cover/src/montecover/plm/plr_ate_tune.py index c50b5ef..6fbe843 100644 --- a/monte-cover/src/montecover/plm/plr_ate_tune.py +++ b/monte-cover/src/montecover/plm/plr_ate_tune.py @@ -73,7 +73,6 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ml_g=ml_g if score == "IV-type" else None, score=score, ) - dml_model.fit() dml_model_tuned = dml.DoubleMLPLR( obj_dml_data=dml_data, @@ -86,12 +85,12 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ml_param_space=self._param_space, optuna_settings=self._optuna_settings, ) - dml_model_tuned.fit() result = { "coverage": [], } for model in [dml_model, dml_model_tuned]: + model.fit() nuisance_loss = model.nuisance_loss for level in self.confidence_parameters["level"]: level_result = dict() diff --git a/results/plm/plpr_ate_config.yml b/results/plm/plpr_ate_config.yml index 5058848..7cb98fe 100644 --- a/results/plm/plpr_ate_config.yml +++ b/results/plm/plpr_ate_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 random_seed: 42 n_jobs: -2 @@ -7,44 +7,26 @@ dgp_parameters: theta: - 0.5 dim_x: - - 5 + - 10 num_id: - 250 num_t: - 10 - dgp_type: + DGP: - dgp1 + - dgp2 + - dgp3 learner_definitions: lasso: &id001 name: LassoCV - rf: &id002 - name: RF Regr. - params: - n_estimators: 200 - max_features: 10 - max_depth: 5 - min_samples_leaf: 20 - lgbm: &id003 + lgbm: &id002 name: LGBM Regr. - params: - n_estimators: 500 - learning_rate: 0.01 dml_parameters: learners: - ml_g: *id001 ml_m: *id001 - ml_g: *id002 ml_m: *id002 - - ml_g: *id001 - ml_m: *id002 - - ml_g: *id002 - ml_m: *id001 - - ml_g: *id003 - ml_m: *id003 - - ml_g: *id003 - ml_m: *id001 - - ml_g: *id001 - ml_m: *id003 score: - partialling out - IV-type diff --git a/results/plm/plpr_ate_coverage.csv b/results/plm/plpr_ate_coverage.csv index 350b61d..1a28083 100644 --- a/results/plm/plpr_ate_coverage.csv +++ b/results/plm/plpr_ate_coverage.csv @@ -1,113 +1,97 @@ -Learner g,Learner m,Score,Approach,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Regr.,IV-type,cre_general,0.9,0.55,0.06976296301749524,0.035198461781479874,100 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,0.95,0.7,0.08312769764353288,0.035198461781479874,100 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,0.9,0.15,0.08315989327247372,0.06366230307558025,100 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,0.95,0.3,0.0990911246457384,0.06366230307558025,100 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,0.9,0.31,0.0787697007587191,0.052629965591665766,100 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,0.95,0.44,0.09385988760970865,0.052629965591665766,100 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,0.9,0.76,0.07059433168196631,0.025855786259495927,100 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,0.95,0.83,0.08411833450844253,0.025855786259495927,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,0.9,0.83,0.07167303051294482,0.02200596075260934,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,0.95,0.89,0.08540368344420271,0.02200596075260934,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,0.9,0.09,0.10588795217996806,0.09367721517282793,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,0.95,0.17,0.1261732772259375,0.09367721517282793,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,0.9,0.44,0.08464465046420193,0.04736213866829519,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,0.95,0.55,0.10086032196147011,0.04736213866829519,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,0.9,0.81,0.06991797298743924,0.020542497542998087,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,0.95,0.89,0.08331240341513273,0.020542497542998087,100 -LGBM Regr.,LassoCV,IV-type,cre_general,0.9,0.88,0.0674577407846109,0.017650643597292594,100 -LGBM Regr.,LassoCV,IV-type,cre_general,0.95,0.94,0.08038085593142984,0.017650643597292594,100 -LGBM Regr.,LassoCV,IV-type,cre_normal,0.9,0.91,0.07307822589614209,0.017418106556931913,100 -LGBM Regr.,LassoCV,IV-type,cre_normal,0.95,0.94,0.08707807701769836,0.017418106556931913,100 -LGBM Regr.,LassoCV,IV-type,fd_exact,0.9,0.84,0.08389361860254477,0.021670659810667466,100 -LGBM Regr.,LassoCV,IV-type,fd_exact,0.95,0.91,0.09996541230144265,0.021670659810667466,100 -LGBM Regr.,LassoCV,IV-type,wg_approx,0.9,0.87,0.06949913189844714,0.016325372697573344,100 -LGBM Regr.,LassoCV,IV-type,wg_approx,0.95,0.96,0.08281332347499758,0.016325372697573344,100 -LGBM Regr.,LassoCV,partialling out,cre_general,0.9,0.9,0.07692721751865365,0.018166428880577665,100 -LGBM Regr.,LassoCV,partialling out,cre_general,0.95,0.94,0.09166443341641374,0.018166428880577665,100 -LGBM Regr.,LassoCV,partialling out,cre_normal,0.9,0.88,0.07726746749794217,0.021145972525086813,100 -LGBM Regr.,LassoCV,partialling out,cre_normal,0.95,0.91,0.09206986627330688,0.021145972525086813,100 -LGBM Regr.,LassoCV,partialling out,fd_exact,0.9,0.84,0.09182622565640873,0.022195273488019994,100 -LGBM Regr.,LassoCV,partialling out,fd_exact,0.95,0.94,0.10941769661071417,0.022195273488019994,100 -LGBM Regr.,LassoCV,partialling out,wg_approx,0.9,0.87,0.07364578026933061,0.017684921488368654,100 -LGBM Regr.,LassoCV,partialling out,wg_approx,0.95,0.91,0.08775435976559208,0.017684921488368654,100 -LassoCV,LGBM Regr.,IV-type,cre_general,0.9,0.84,0.06408862713224175,0.016935357490954327,100 -LassoCV,LGBM Regr.,IV-type,cre_general,0.95,0.9,0.07636630940262763,0.016935357490954327,100 -LassoCV,LGBM Regr.,IV-type,cre_normal,0.9,0.47,0.077609959538598,0.04105004731485136,100 -LassoCV,LGBM Regr.,IV-type,cre_normal,0.95,0.62,0.09247797071109883,0.04105004731485136,100 -LassoCV,LGBM Regr.,IV-type,fd_exact,0.9,0.83,0.07205612294713466,0.021555328315601963,100 -LassoCV,LGBM Regr.,IV-type,fd_exact,0.95,0.92,0.08586016623480423,0.021555328315601963,100 -LassoCV,LGBM Regr.,IV-type,wg_approx,0.9,0.85,0.0663987346552934,0.01624006953577458,100 -LassoCV,LGBM Regr.,IV-type,wg_approx,0.95,0.93,0.07911897229700798,0.01624006953577458,100 -LassoCV,LGBM Regr.,partialling out,cre_general,0.9,0.05,0.06557547934131532,0.06264380325895827,100 -LassoCV,LGBM Regr.,partialling out,cre_general,0.95,0.08,0.07813800308549902,0.06264380325895827,100 -LassoCV,LGBM Regr.,partialling out,cre_normal,0.9,0.71,0.10081547579356802,0.03675977007461054,100 -LassoCV,LGBM Regr.,partialling out,cre_normal,0.95,0.86,0.12012904881140074,0.03675977007461054,100 -LassoCV,LGBM Regr.,partialling out,fd_exact,0.9,0.15,0.07695876065150617,0.06364910279874753,100 -LassoCV,LGBM Regr.,partialling out,fd_exact,0.95,0.24,0.09170201937746068,0.06364910279874753,100 -LassoCV,LGBM Regr.,partialling out,wg_approx,0.9,0.26,0.06589899215410072,0.04437018489771354,100 -LassoCV,LGBM Regr.,partialling out,wg_approx,0.95,0.41,0.07852349237841062,0.04437018489771354,100 -LassoCV,LassoCV,IV-type,cre_general,0.9,0.84,0.06269129562323468,0.01563599345348165,100 -LassoCV,LassoCV,IV-type,cre_general,0.95,0.89,0.07470128621318266,0.01563599345348165,100 -LassoCV,LassoCV,IV-type,cre_normal,0.9,0.85,0.06783477131206325,0.016103911796709967,100 -LassoCV,LassoCV,IV-type,cre_normal,0.95,0.93,0.0808301155146356,0.016103911796709967,100 -LassoCV,LassoCV,IV-type,fd_exact,0.9,0.85,0.07873803535290637,0.019789144722074756,100 -LassoCV,LassoCV,IV-type,fd_exact,0.95,0.91,0.09382215595144322,0.019789144722074756,100 -LassoCV,LassoCV,IV-type,wg_approx,0.9,0.86,0.0665166364032729,0.015804075306866137,100 -LassoCV,LassoCV,IV-type,wg_approx,0.95,0.94,0.0792594608948794,0.015804075306866137,100 -LassoCV,LassoCV,partialling out,cre_general,0.9,0.86,0.06881093339070665,0.01546112637838432,100 -LassoCV,LassoCV,partialling out,cre_general,0.95,0.93,0.08199328437408043,0.01546112637838432,100 -LassoCV,LassoCV,partialling out,cre_normal,0.9,0.89,0.06912204449132515,0.01857538888976107,100 -LassoCV,LassoCV,partialling out,cre_normal,0.95,0.91,0.08236399611548505,0.01857538888976107,100 -LassoCV,LassoCV,partialling out,fd_exact,0.9,0.88,0.08276609543947541,0.019659160874541663,100 -LassoCV,LassoCV,partialling out,fd_exact,0.95,0.91,0.09862188558566647,0.019659160874541663,100 -LassoCV,LassoCV,partialling out,wg_approx,0.9,0.85,0.06888372896371994,0.015753922092875072,100 -LassoCV,LassoCV,partialling out,wg_approx,0.95,0.94,0.0820800256494147,0.015753922092875072,100 -LassoCV,RF Regr.,IV-type,cre_general,0.9,0.87,0.063427748689378,0.016360020449820684,100 -LassoCV,RF Regr.,IV-type,cre_general,0.95,0.89,0.07557882416689116,0.016360020449820684,100 -LassoCV,RF Regr.,IV-type,cre_normal,0.9,0.15,0.0875082261853324,0.07259188695606707,100 -LassoCV,RF Regr.,IV-type,cre_normal,0.95,0.26,0.10427248289084172,0.07259188695606707,100 -LassoCV,RF Regr.,IV-type,fd_exact,0.9,0.24,0.05940304754467235,0.046424969266456985,100 -LassoCV,RF Regr.,IV-type,fd_exact,0.95,0.31,0.07078309695876256,0.046424969266456985,100 -LassoCV,RF Regr.,IV-type,wg_approx,0.9,0.83,0.06324687203517945,0.015898907196209247,100 -LassoCV,RF Regr.,IV-type,wg_approx,0.95,0.9,0.07536329633993775,0.015898907196209247,100 -LassoCV,RF Regr.,partialling out,cre_general,0.9,0.1,0.0662836421225766,0.05419672745286945,100 -LassoCV,RF Regr.,partialling out,cre_general,0.95,0.2,0.07898183108558451,0.05419672745286945,100 -LassoCV,RF Regr.,partialling out,cre_normal,0.9,0.8,0.11775140213448282,0.03696953940696344,100 -LassoCV,RF Regr.,partialling out,cre_normal,0.95,0.88,0.1403094497474626,0.03696953940696344,100 -LassoCV,RF Regr.,partialling out,fd_exact,0.9,0.0,0.07167176534213965,0.13858159828069488,100 -LassoCV,RF Regr.,partialling out,fd_exact,0.95,0.0,0.08540217590020496,0.13858159828069488,100 -LassoCV,RF Regr.,partialling out,wg_approx,0.9,0.33,0.06651972180573086,0.04054256033746144,100 -LassoCV,RF Regr.,partialling out,wg_approx,0.95,0.47,0.0792631373786688,0.04054256033746144,100 -RF Regr.,LassoCV,IV-type,cre_general,0.9,0.84,0.06731805601499356,0.018440143152441246,100 -RF Regr.,LassoCV,IV-type,cre_general,0.95,0.88,0.08021441126234022,0.018440143152441246,100 -RF Regr.,LassoCV,IV-type,cre_normal,0.9,0.91,0.07296368195845183,0.016617103105959666,100 -RF Regr.,LassoCV,IV-type,cre_normal,0.95,0.92,0.08694158949756786,0.016617103105959666,100 -RF Regr.,LassoCV,IV-type,fd_exact,0.9,0.79,0.09326073010447117,0.02542441248522852,100 -RF Regr.,LassoCV,IV-type,fd_exact,0.95,0.93,0.11112701408905766,0.02542441248522852,100 -RF Regr.,LassoCV,IV-type,wg_approx,0.9,0.87,0.06950553766794654,0.01588666162575094,100 -RF Regr.,LassoCV,IV-type,wg_approx,0.95,0.93,0.08282095642014621,0.01588666162575094,100 -RF Regr.,LassoCV,partialling out,cre_general,0.9,0.86,0.07741195562828165,0.01982281120433611,100 -RF Regr.,LassoCV,partialling out,cre_general,0.95,0.92,0.09224203449971849,0.01982281120433611,100 -RF Regr.,LassoCV,partialling out,cre_normal,0.9,0.88,0.0781718182813771,0.019678622679720436,100 -RF Regr.,LassoCV,partialling out,cre_normal,0.95,0.93,0.09314746669676108,0.019678622679720436,100 -RF Regr.,LassoCV,partialling out,fd_exact,0.9,0.83,0.11548693628175762,0.02877950068391491,100 -RF Regr.,LassoCV,partialling out,fd_exact,0.95,0.93,0.13761117225769717,0.02877950068391491,100 -RF Regr.,LassoCV,partialling out,wg_approx,0.9,0.85,0.07543409059934668,0.01718244028421878,100 -RF Regr.,LassoCV,partialling out,wg_approx,0.95,0.91,0.08988526295514129,0.01718244028421878,100 -RF Regr.,RF Regr.,IV-type,cre_general,0.9,0.1,0.06987715684699204,0.06514036136312265,100 -RF Regr.,RF Regr.,IV-type,cre_general,0.95,0.14,0.08326376798401978,0.06514036136312265,100 -RF Regr.,RF Regr.,IV-type,cre_normal,0.9,0.04,0.09171285099948878,0.10869001408855256,100 -RF Regr.,RF Regr.,IV-type,cre_normal,0.95,0.04,0.10928260237455742,0.10869001408855256,100 -RF Regr.,RF Regr.,IV-type,fd_exact,0.9,0.0,0.07150977117827961,0.26114872821547735,100 -RF Regr.,RF Regr.,IV-type,fd_exact,0.95,0.0,0.08520914794825293,0.26114872821547735,100 -RF Regr.,RF Regr.,IV-type,wg_approx,0.9,0.13,0.0686128585359986,0.05618482624012591,100 -RF Regr.,RF Regr.,IV-type,wg_approx,0.95,0.2,0.08175726362724335,0.05618482624012591,100 -RF Regr.,RF Regr.,partialling out,cre_general,0.9,0.1,0.07231527224572468,0.062450994369805814,100 -RF Regr.,RF Regr.,partialling out,cre_general,0.95,0.16,0.08616896167017474,0.062450994369805814,100 -RF Regr.,RF Regr.,partialling out,cre_normal,0.9,0.17,0.12352151933218738,0.09185302990567756,100 -RF Regr.,RF Regr.,partialling out,cre_normal,0.95,0.22,0.14718496846157234,0.09185302990567756,100 -RF Regr.,RF Regr.,partialling out,fd_exact,0.9,0.0,0.08475585681151454,0.28050869887372326,100 -RF Regr.,RF Regr.,partialling out,fd_exact,0.95,0.0,0.10099283249737045,0.28050869887372326,100 -RF Regr.,RF Regr.,partialling out,wg_approx,0.9,0.13,0.072067489649116,0.05947757489790515,100 -RF Regr.,RF Regr.,partialling out,wg_approx,0.95,0.22,0.08587371049560737,0.05947757489790515,100 +Learner g,Learner m,Score,Approach,DGP,level,Coverage,CI Length,Bias,Loss g,Loss m,repetition +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.9,0.685,0.07825958532713045,0.03043935901390476,1.5482634245331157,1.0388990792815624,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.95,0.795,0.09325204758218905,0.03043935901390476,1.5482634245331157,1.0388990792815624,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.9,0.9,0.07568235712288829,0.019227801904568253,1.5453227852715608,1.014488281057047,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.95,0.945,0.0901810907642153,0.019227801904568253,1.5453227852715608,1.014488281057047,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.9,0.0,0.09459012254838152,0.2898336465542013,1.6803016907278263,1.2538635096584279,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.95,0.0,0.11271108288927856,0.2898336465542013,1.6803016907278263,1.2538635096584279,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.9,0.405,0.0914507937941699,0.052651499081212826,1.5346644973650363,1.0715450800990285,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.95,0.55,0.1089703419546037,0.052651499081212826,1.5346644973650363,1.0715450800990285,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.9,0.875,0.09376358856769959,0.02562869058928203,1.5417071621936826,1.0365404860260867,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.95,0.935,0.11172620690542732,0.02562869058928203,1.5417071621936826,1.0365404860260867,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.9,0.0,0.10373060826155252,0.21990420865980828,1.7204436818144588,1.4734592546679581,200 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.95,0.0,0.1236026433937967,0.21990420865980828,1.7204436818144588,1.4734592546679581,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.9,0.475,0.0845922654157919,0.04556366892982161,1.5592571956779957,1.560381433558009,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.95,0.595,0.10079790132626604,0.04556366892982161,1.5592571956779957,1.560381433558009,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.9,0.85,0.0874896273640717,0.02440724171781883,1.503506135854519,1.51547345213435,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.95,0.92,0.10425032102839481,0.02440724171781883,1.503506135854519,1.51547345213435,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.9,0.0,0.08785625331373677,0.39048686946534206,1.8332923873454399,2.0339019268207594,200 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.95,0.0,0.10468718279248568,0.39048686946534206,1.8332923873454399,2.0339019268207594,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.9,0.755,0.0773198192870836,0.025688659094466836,1.0206898951802998,1.0208011908233934,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.95,0.825,0.09213224727764806,0.025688659094466836,1.0206898951802998,1.0208011908233934,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.9,0.9,0.06915096912963344,0.016904622144843975,1.0166398752165657,1.1183173279947765,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.95,0.96,0.08239846194783704,0.016904622144843975,1.0166398752165657,1.1183173279947765,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.9,0.0,0.0644883030727697,0.6668132448114582,1.3111193828356513,1.7845823260498657,200 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.95,0.0,0.07684255265983088,0.6668132448114582,1.3111193828356513,1.7845823260498657,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,0.88,0.07276301215662687,0.01883933964227799,1.827594776452371,1.0388491161653157,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,0.935,0.08670247668052608,0.01883933964227799,1.827594776452371,1.0388491161653157,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,0.53,0.07032087926180335,0.035930388703310105,1.8138875087628077,1.0140533010272026,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,0.62,0.08379249585251419,0.035930388703310105,1.8138875087628077,1.0140533010272026,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,0.0,0.14644793400955328,0.24387897456620083,2.2265021730931096,1.2509985001043957,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,0.0,0.1745034765196716,0.24387897456620083,2.2265021730931096,1.2509985001043957,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,0.34,0.10671440925189961,0.06604278972783294,1.8278662384821258,1.0723966288109512,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,0.475,0.1271580615673603,0.06604278972783294,1.8278662384821258,1.0723966288109512,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,0.885,0.09882487285553931,0.02624515366426892,1.814110699387459,1.0369175101803143,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,0.92,0.11775709911196953,0.02624515366426892,1.814110699387459,1.0369175101803143,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,0.16,0.1449329301123928,0.11835881606523448,2.227210435501958,1.472624525509306,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,0.24,0.17269823803144477,0.11835881606523448,2.227210435501958,1.472624525509306,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,0.755,0.08348771770764904,0.029398969242520855,1.7917213036062813,1.5606673925355516,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,0.835,0.09948175155360901,0.029398969242520855,1.7917213036062813,1.5606673925355516,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,0.64,0.08083241423212986,0.033634783973774104,1.685790636282,1.514365604383286,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,0.725,0.09631776231178987,0.033634783973774104,1.685790636282,1.514365604383286,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,0.0,0.13794149985824275,0.3445870684575309,2.6732063767531145,2.03525478663509,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,0.0,0.16436743505054094,0.3445870684575309,2.6732063767531145,2.03525478663509,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,0.875,0.070192708182761,0.01790301329858119,1.1521347825360824,1.0215954484316985,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,0.945,0.08363977059193994,0.01790301329858119,1.1521347825360824,1.0215954484316985,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,0.65,0.062346536560795245,0.024534437530928276,1.1597307643399963,1.1183251465249968,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,0.77,0.07429048045232146,0.024534437530928276,1.1597307643399963,1.1183251465249968,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,0.0,0.0757314775470122,0.634032650278728,2.495996501856889,1.7833391473200728,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,0.0,0.09023962135964987,0.634032650278728,2.495996501856889,1.7833391473200728,200 +LassoCV,LassoCV,IV-type,cre_general,dgp1,0.9,0.88,0.061984516414491944,0.016324731772937556,1.4342287909763354,0.9510592759499467,200 +LassoCV,LassoCV,IV-type,cre_general,dgp1,0.95,0.93,0.07385910684143827,0.016324731772937556,1.4342287909763354,0.9510592759499467,200 +LassoCV,LassoCV,IV-type,cre_general,dgp2,0.9,0.905,0.057620160966157796,0.013803833825012301,1.453044641353273,1.1603287550933408,200 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+LassoCV,LassoCV,IV-type,cre_normal,dgp3,0.9,0.0,0.04085404898658369,0.8535341484905211,1.8241998246103128,2.5623401395090957,200 +LassoCV,LassoCV,IV-type,cre_normal,dgp3,0.95,0.0,0.04868060192367591,0.8535341484905211,1.8241998246103128,2.5623401395090957,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.9,0.875,0.07699906791932538,0.01896304486978907,1.4187863225136041,1.4168940208398721,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.95,0.92,0.09175004844943789,0.01896304486978907,1.4187863225136041,1.4168940208398721,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.9,0.855,0.0693854583826273,0.01846259710560201,1.4454320352975885,1.729399172844623,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.95,0.95,0.08267787312649703,0.01846259710560201,1.4454320352975885,1.729399172844623,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.9,0.0,0.0472369281538713,0.8568418398893103,1.9590835619912503,3.813136954208584,200 +LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.95,0.0,0.05628627155930228,0.8568418398893103,1.9590835619912503,3.813136954208584,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.9,0.89,0.06553272978973308,0.01649413851419959,0.9495371672185499,0.9494711664131666,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.95,0.95,0.07808706385292322,0.01649413851419959,0.9495371672185499,0.9494711664131666,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.9,0.915,0.05763672107912186,0.013769696120827644,0.9686100757517228,1.160106460884591,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.95,0.955,0.06867838915942166,0.013769696120827644,0.9686100757517228,1.160106460884591,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.9,0.0,0.04058903382821993,0.8580755964454261,1.3122692024152842,2.5598958059389676,200 +LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.95,0.0,0.048364816885275365,0.8580755964454261,1.3122692024152842,2.5598958059389676,200 +LassoCV,LassoCV,partialling out,cre_general,dgp1,0.9,0.92,0.06920888211739959,0.01626175159042426,1.684343741909081,0.9510467509478506,200 +LassoCV,LassoCV,partialling out,cre_general,dgp1,0.95,0.955,0.08246746952905819,0.01626175159042426,1.684343741909081,0.9510467509478506,200 +LassoCV,LassoCV,partialling out,cre_general,dgp2,0.9,0.905,0.05764844482807034,0.01375543405306076,1.7404542066208524,1.160316313324535,200 +LassoCV,LassoCV,partialling out,cre_general,dgp2,0.95,0.955,0.06869235886792724,0.01375543405306076,1.7404542066208524,1.160316313324535,200 +LassoCV,LassoCV,partialling out,cre_general,dgp3,0.9,0.0,0.041257696186529164,0.8563945872281358,4.179336039267118,2.5599813293410465,200 +LassoCV,LassoCV,partialling out,cre_general,dgp3,0.95,0.0,0.04916157722834166,0.8563945872281358,4.179336039267118,2.5599813293410465,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.9,0.855,0.06973459900789133,0.019049215268563232,1.684460377521119,0.95023402530816,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.95,0.915,0.0830938998414854,0.019049215268563232,1.684460377521119,0.95023402530816,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.9,0.785,0.058444469093490825,0.01927295953920844,1.7408614068721548,1.1610697816791573,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.95,0.86,0.06964088028374202,0.01927295953920844,1.7408614068721548,1.1610697816791573,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.9,0.0,0.04146212164876396,0.869985092359518,4.17938815750255,2.56234053481489,200 +LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.95,0.0,0.04940516519078288,0.869985092359518,4.17938815750255,2.56234053481489,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.9,0.91,0.08328733682400252,0.018777053525699566,1.587407608777859,1.4168585664407807,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.95,0.94,0.09924298300381082,0.018777053525699566,1.587407608777859,1.4168585664407807,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.9,0.855,0.06940217263668914,0.018403160621699194,1.6828094791577712,1.7291837722637475,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.95,0.945,0.08269778938862084,0.018403160621699194,1.6828094791577712,1.7291837722637475,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.9,0.0,0.048485297166406933,0.8545169346117363,5.5367265308249305,3.8138807020036554,200 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.95,0.0,0.05777379498624722,0.8545169346117363,5.5367265308249305,3.8138807020036554,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.9,0.915,0.06936657422584458,0.016332149322083853,1.0625458956357716,0.9495386148267948,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.95,0.965,0.08265537126580495,0.016332149322083853,1.0625458956357716,0.9495386148267948,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.9,0.915,0.05762913503140869,0.01374336683877195,1.1283657839873227,1.1601948185334325,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.95,0.955,0.06866934982603712,0.01374336683877195,1.1283657839873227,1.1601948185334325,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.9,0.0,0.041296819938919045,0.8567445476085612,3.718004034724151,2.559895975727974,200 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.95,0.0,0.04920819605470273,0.8567445476085612,3.718004034724151,2.559895975727974,200 diff --git a/results/plm/plpr_ate_metadata.csv b/results/plm/plpr_ate_metadata.csv index 3151396..9a468cf 100644 --- a/results/plm/plpr_ate_metadata.csv +++ b/results/plm/plpr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLPRATECoverageSimulation,2025-12-05 16:00,24.24196228583654,3.12.9,scripts/plm/plpr_ate_config.yml +0.12.dev0,PLPRATECoverageSimulation,2025-12-08 13:35,8.844934634367625,3.12.9,scripts/plm/plpr_ate_config.yml diff --git a/results/plm/plpr_ate_tune_config.yml b/results/plm/plpr_ate_tune_config.yml new file mode 100644 index 0000000..e69a8e8 --- /dev/null +++ b/results/plm/plpr_ate_tune_config.yml @@ -0,0 +1,36 @@ +simulation_parameters: + repetitions: 100 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + theta: + - 0.5 + dim_x: + - 10 + num_id: + - 250 + num_t: + - 10 + DGP: + - dgp1 + - dgp2 + - dgp3 +learner_definitions: + lgbm: &id001 + name: LGBM Regr. +dml_parameters: + learners: + - ml_g: *id001 + ml_m: *id001 + score: + - partialling out + approach: + - cre_general + - cre_normal + - fd_exact + - wg_approx +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/plm/plpr_ate_tune_coverage.csv b/results/plm/plpr_ate_tune_coverage.csv new file mode 100644 index 0000000..7981d57 --- /dev/null +++ b/results/plm/plpr_ate_tune_coverage.csv @@ -0,0 +1,49 @@ +Learner g,Learner m,Score,Approach,DGP,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.84,0.07214938085352593,0.020958634872141193,1.8380925400044046,1.0425563302438159,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.84,0.07214938085352593,0.020958634872141193,1.8406447982984568,1.0536808246925673,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.87,0.08597128988423149,0.020958634872141193,1.8380925400044046,1.0425563302438159,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.87,0.08597128988423149,0.020958634872141193,1.8406447982984568,1.0536808246925673,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,False,0.56,0.07019217379642241,0.03223609623930599,1.8417454099789756,1.0114586592127623,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,True,0.56,0.07019217379642241,0.03223609623930599,1.845874406802228,1.0235401498732124,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,False,0.64,0.08363913383134296,0.03223609623930599,1.8417454099789756,1.0114586592127623,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,True,0.64,0.08363913383134296,0.03223609623930599,1.845874406802228,1.0235401498732124,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.14506683792132763,0.24426316655751223,2.2357614775306525,1.2646810617814317,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.0,0.14506683792132763,0.24426316655751223,2.2163775352398476,1.2616692581843845,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.17285779902730514,0.24426316655751223,2.2357614775306525,1.2646810617814317,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.0,0.17285779902730514,0.24426316655751223,2.2163775352398476,1.2616692581843845,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.37,0.10564657766155527,0.0639652478345845,1.8351842533954925,1.075452347488812,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.37,0.10564657766155527,0.0639652478345845,1.84118987710939,1.0701310013996495,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.48,0.12588566174750032,0.0639652478345845,1.8351842533954925,1.075452347488812,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.48,0.12588566174750032,0.0639652478345845,1.84118987710939,1.0701310013996495,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,False,0.9,0.09978560477366961,0.025742513859099113,1.8385051319239296,1.0355345852928854,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,True,0.9,0.09978560477366961,0.025742513859099113,1.8419745101738403,1.020917516314259,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,False,0.94,0.11890188180112794,0.025742513859099113,1.8385051319239296,1.0355345852928854,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,True,0.94,0.11890188180112794,0.025742513859099113,1.8419745101738403,1.020917516314259,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.21,0.14736942212785784,0.11116202396744745,2.2330986160573976,1.4764474809056063,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.21,0.14736942212785784,0.11116202396744745,2.234545448937938,1.4524951630618492,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.35,0.17560149733712627,0.11116202396744745,2.2330986160573976,1.4764474809056063,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.35,0.17560149733712627,0.11116202396744745,2.234545448937938,1.4524951630618492,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.75,0.08338178511761866,0.027758914202157547,1.7857580223977323,1.5617592387653514,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.75,0.08338178511761866,0.027758914202157547,1.7755675591454931,1.5446407468542838,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.88,0.09935552508710367,0.027758914202157547,1.7857580223977323,1.5617592387653514,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.88,0.09935552508710367,0.027758914202157547,1.7755675591454931,1.5446407468542838,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,False,0.75,0.08172180822747643,0.0288237206890361,1.6839348474894564,1.5107731514052591,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,True,0.75,0.08172180822747643,0.0288237206890361,1.6339834968196845,1.4811015397253107,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,False,0.81,0.0973775406229921,0.0288237206890361,1.6839348474894564,1.5107731514052591,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,True,0.81,0.0973775406229921,0.0288237206890361,1.6339834968196845,1.4811015397253107,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.13951582228199874,0.3374665458880539,2.6600546733155745,2.0403787961336346,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.13951582228199874,0.3374665458880539,2.6731472910158462,2.0516197504849605,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.16624335592280381,0.3374665458880539,2.6600546733155745,2.0403787961336346,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.16624335592280381,0.3374665458880539,2.6731472910158462,2.0516197504849605,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.87,0.06986442022400832,0.017559392666425425,1.154335223709233,1.0265129772476522,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.87,0.06986442022400832,0.017559392666425425,1.1343289505288514,1.0057938448034482,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.95,0.08324859136166037,0.017559392666425425,1.154335223709233,1.0265129772476522,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.95,0.08324859136166037,0.017559392666425425,1.1343289505288514,1.0057938448034482,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,False,0.63,0.06201367415287924,0.02529730149300308,1.1565802541819723,1.1157816130918814,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,True,0.63,0.06201367415287924,0.02529730149300308,1.1144561853005877,1.0769038366613766,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,False,0.73,0.07389385042966602,0.02529730149300308,1.1565802541819723,1.1157816130918814,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,True,0.73,0.07389385042966602,0.02529730149300308,1.1144561853005877,1.0769038366613766,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.07574491665108128,0.6306933353030978,2.494072876419393,1.7857864356531934,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.07574491665108128,0.6306933353030978,2.560473569307849,1.834203386940789,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.09025563503985116,0.6306933353030978,2.494072876419393,1.7857864356531934,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.09025563503985116,0.6306933353030978,2.560473569307849,1.834203386940789,100 diff --git a/results/plm/plpr_ate_tune_metadata.csv b/results/plm/plpr_ate_tune_metadata.csv new file mode 100644 index 0000000..9e9111a --- /dev/null +++ b/results/plm/plpr_ate_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.12.dev0,PLPRATETuningCoverageSimulation,2025-12-08 14:50,62.522164789835614,3.12.9,scripts/plm/plpr_ate_tune_config.yml diff --git a/scripts/plm/plpr_ate_config.yml b/scripts/plm/plpr_ate_config.yml index 2b4ba35..dd1be50 100644 --- a/scripts/plm/plpr_ate_config.yml +++ b/scripts/plm/plpr_ate_config.yml @@ -1,54 +1,32 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: theta: [0.5] # Treatment effect - dim_x: [5] # Number of covariates + dim_x: [10] # Number of covariates num_id: [250] # number of units num_t: [10] # number of time periods - dgp_type: ["dgp1"] - + DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes # Define reusable learner configurations learner_definitions: lasso: &lasso name: "LassoCV" - rf: &rf - name: "RF Regr." - params: - n_estimators: 200 - max_features: 10 - max_depth: 5 - min_samples_leaf: 20 - lgbm: &lgbm name: "LGBM Regr." - params: - n_estimators: 500 - learning_rate: 0.01 dml_parameters: learners: - ml_g: *lasso ml_m: *lasso - - ml_g: *rf - ml_m: *rf - - ml_g: *lasso - ml_m: *rf - - ml_g: *rf - ml_m: *lasso - ml_g: *lgbm ml_m: *lgbm - - ml_g: *lgbm - ml_m: *lasso - - ml_g: *lasso - ml_m: *lgbm score: ["partialling out", "IV-type"] approach: ["cre_general", "cre_normal", "fd_exact", "wg_approx"] diff --git a/scripts/plm/plpr_ate_tune.py b/scripts/plm/plpr_ate_tune.py new file mode 100644 index 0000000..4b63719 --- /dev/null +++ b/scripts/plm/plpr_ate_tune.py @@ -0,0 +1,13 @@ +from montecover.plm import PLPRATETuningCoverageSimulation + +# Create and run simulation with config file +sim = PLPRATETuningCoverageSimulation( + config_file="scripts/plm/plpr_ate_tune_config.yml", + log_level="INFO", + log_file="logs/plm/plpr_ate_tune_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/plm/", file_prefix="plpr_ate_tune") + +# Save config file for reproducibility +sim.save_config("results/plm/plpr_ate_tune_config.yml") diff --git a/scripts/plm/plpr_ate_tune_config.yml b/scripts/plm/plpr_ate_tune_config.yml new file mode 100644 index 0000000..a2f4e3d --- /dev/null +++ b/scripts/plm/plpr_ate_tune_config.yml @@ -0,0 +1,30 @@ +# Simulation parameters for PLR ATE Coverage + +simulation_parameters: + repetitions: 100 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + theta: [0.5] # Treatment effect + dim_x: [10] # Number of covariates + num_id: [250] # number of units + num_t: [10] # number of time periods + DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes + +# Define reusable learner configurations +learner_definitions: + lgbm: &lgbm + name: "LGBM Regr." + +dml_parameters: + learners: + - ml_g: *lgbm + ml_m: *lgbm + + score: ["partialling out"] + approach: ["cre_general", "cre_normal", "fd_exact", "wg_approx"] + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From 7a5025cd1d1dba39502c422284bac48c6ee96598 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 8 Dec 2025 16:11:49 +0100 Subject: [PATCH 3/5] updated plr losses --- doc/plm/plr.qmd | 6 ++-- results/plm/plr_ate_coverage.csv | 58 ++++++++++++++++---------------- results/plm/plr_ate_metadata.csv | 2 +- 3 files changed, 33 insertions(+), 33 deletions(-) diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd index 2fe0aaf..03ccd28 100644 --- a/doc/plm/plr.qmd +++ b/doc/plm/plr.qmd @@ -50,7 +50,7 @@ elif "n_rep" in df_coverage.columns and df_coverage["n_rep"].nunique() == 1: else: n_rep_coverage = "N/A" # Fallback if n_rep cannot be determined -display_columns_coverage = ["Learner g", "Learner m", "Bias", "CI Length", "Coverage"] +display_columns_coverage = ["Learner g", "Learner m", "Bias", "CI Length", "Coverage", "Loss g", "Loss m"] ``` ### Partialling out @@ -64,7 +64,7 @@ generate_and_show_styled_table( display_cols=display_columns_coverage, n_rep=n_rep_coverage, level_col="level", - rename_map={"Learner g": "Learner l"}, + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -78,7 +78,7 @@ generate_and_show_styled_table( display_cols=display_columns_coverage, n_rep=n_rep_coverage, level_col="level", - rename_map={"Learner g": "Learner l"}, + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, coverage_highlight_cols=["Coverage"] ) ``` diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv index 305cb65..6576238 100644 --- a/results/plm/plr_ate_coverage.csv +++ b/results/plm/plr_ate_coverage.csv @@ -1,29 +1,29 @@ -Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Regr.,IV-type,0.9,0.889,0.15978309138485988,0.040678761104429376,1000 -LGBM Regr.,LGBM Regr.,IV-type,0.95,0.939,0.1903932965957687,0.040678761104429376,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.9,0.838,0.14681770731506133,0.041275148836012944,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.95,0.897,0.17494408858956342,0.041275148836012944,1000 -LGBM Regr.,LassoCV,IV-type,0.9,0.876,0.14833747639697978,0.039489073815308307,1000 -LGBM Regr.,LassoCV,IV-type,0.95,0.931,0.17675500514564524,0.039489073815308307,1000 -LGBM Regr.,LassoCV,partialling out,0.9,0.893,0.15937942718304723,0.03925857893846666,1000 -LGBM Regr.,LassoCV,partialling out,0.95,0.94,0.18991230103212867,0.03925857893846666,1000 -LassoCV,LGBM Regr.,IV-type,0.9,0.878,0.15066272181786255,0.03855046266735263,1000 -LassoCV,LGBM Regr.,IV-type,0.95,0.929,0.17952570595784667,0.03855046266735263,1000 -LassoCV,LGBM Regr.,partialling out,0.9,0.526,0.13905172633298185,0.06966076055623882,1000 -LassoCV,LGBM Regr.,partialling out,0.95,0.642,0.1656903514909566,0.06966076055623882,1000 -LassoCV,LassoCV,IV-type,0.9,0.877,0.13992145634265207,0.0366445077678926,1000 -LassoCV,LassoCV,IV-type,0.95,0.928,0.16672669871802667,0.0366445077678926,1000 -LassoCV,LassoCV,partialling out,0.9,0.897,0.14673266782178596,0.03648225251216086,1000 -LassoCV,LassoCV,partialling out,0.95,0.945,0.17484275778337358,0.03648225251216086,1000 -LassoCV,RF Regr.,IV-type,0.9,0.855,0.13032149928473868,0.037166678834479695,1000 -LassoCV,RF Regr.,IV-type,0.95,0.907,0.15528764433753836,0.037166678834479695,1000 -LassoCV,RF Regr.,partialling out,0.9,0.775,0.14270313357843983,0.04570634853982781,1000 -LassoCV,RF Regr.,partialling out,0.95,0.862,0.1700412715830077,0.04570634853982781,1000 -RF Regr.,LassoCV,IV-type,0.9,0.873,0.14101035943967402,0.03685517557846389,1000 -RF Regr.,LassoCV,IV-type,0.95,0.937,0.16802420678673716,0.03685517557846389,1000 -RF Regr.,LassoCV,partialling out,0.9,0.895,0.15051386498604236,0.037526282976942105,1000 -RF Regr.,LassoCV,partialling out,0.95,0.938,0.1793483321025444,0.037526282976942105,1000 -RF Regr.,RF Regr.,IV-type,0.9,0.837,0.13142137550510022,0.037870665143070414,1000 -RF Regr.,RF Regr.,IV-type,0.95,0.924,0.15659822768917436,0.037870665143070414,1000 -RF Regr.,RF Regr.,partialling out,0.9,0.884,0.1422661253784088,0.03699055268836001,1000 -RF Regr.,RF Regr.,partialling out,0.95,0.936,0.16952054419488322,0.03699055268836001,1000 +Learner g,Learner m,Score,level,Coverage,CI Length,Bias,Loss g,Loss m,repetition +LGBM Regr.,LGBM Regr.,IV-type,0.9,0.883,0.15928202279086665,0.041454930462256984,1.069765598118696,1.0865571968126624,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.95,0.937,0.18979623654013872,0.041454930462256984,1.069765598118696,1.0865571968126624,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.9,0.842,0.14626443603990613,0.04159012092162225,1.2055345892536342,1.086959351307916,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.95,0.907,0.17428482520270844,0.04159012092162225,1.2055345892536342,1.086959351307916,1000 +LGBM Regr.,LassoCV,IV-type,0.9,0.887,0.147905408377066,0.03847587695058279,1.0693318230438509,1.0086829211963027,1000 +LGBM Regr.,LassoCV,IV-type,0.95,0.948,0.17624016434520753,0.03847587695058279,1.0693318230438509,1.0086829211963027,1000 +LGBM Regr.,LassoCV,partialling out,0.9,0.899,0.15875140474791682,0.03924849266305699,1.2056443309418285,1.0084514460661456,1000 +LGBM Regr.,LassoCV,partialling out,0.95,0.951,0.189163966144349,0.03924849266305699,1.2056443309418285,1.0084514460661456,1000 +LassoCV,LGBM Regr.,IV-type,0.9,0.894,0.15032363130014348,0.03814520647938802,1.0109435769879314,1.0867218776097132,1000 +LassoCV,LGBM Regr.,IV-type,0.95,0.938,0.1791216546846278,0.03814520647938802,1.0109435769879314,1.0867218776097132,1000 +LassoCV,LGBM Regr.,partialling out,0.9,0.498,0.13861728700492693,0.07095098845194882,1.1287372870798746,1.0879589553258844,1000 +LassoCV,LGBM Regr.,partialling out,0.95,0.624,0.16517268510259017,0.07095098845194882,1.1287372870798746,1.0879589553258844,1000 +LassoCV,LassoCV,IV-type,0.9,0.876,0.1395941022128785,0.03614102732438801,1.008526334816832,1.00850722502704,1000 +LassoCV,LassoCV,IV-type,0.95,0.939,0.16633663221360717,0.03614102732438801,1.008526334816832,1.00850722502704,1000 +LassoCV,LassoCV,partialling out,0.9,0.908,0.14656359193331914,0.0355408406947952,1.1286065731474382,1.0083310548041704,1000 +LassoCV,LassoCV,partialling out,0.95,0.957,0.17464129143608328,0.0355408406947952,1.1286065731474382,1.0083310548041704,1000 +LassoCV,RF Regr.,IV-type,0.9,0.844,0.12999176189584266,0.036265043441751715,1.0092786969519125,1.0524458002714516,1000 +LassoCV,RF Regr.,IV-type,0.95,0.908,0.1548947380047176,0.036265043441751715,1.0092786969519125,1.0524458002714516,1000 +LassoCV,RF Regr.,partialling out,0.9,0.78,0.1426021188866897,0.04661398743097048,1.128740578878581,1.0523978387050963,1000 +LassoCV,RF Regr.,partialling out,0.95,0.863,0.16992090515374272,0.04661398743097048,1.128740578878581,1.0523978387050963,1000 +RF Regr.,LassoCV,IV-type,0.9,0.885,0.1406646493212081,0.03547371211869198,1.0162369561093956,1.0085999646131845,1000 +RF Regr.,LassoCV,IV-type,0.95,0.941,0.16761226777272276,0.03547371211869198,1.0162369561093956,1.0085999646131845,1000 +RF Regr.,LassoCV,partialling out,0.9,0.895,0.15031102850606376,0.03675855799694686,1.153850269585127,1.0083949164585309,1000 +RF Regr.,LassoCV,partialling out,0.95,0.946,0.17910663752924322,0.03675855799694686,1.153850269585127,1.0083949164585309,1000 +RF Regr.,RF Regr.,IV-type,0.9,0.845,0.13119958082752003,0.036530036698981375,1.0166818516109433,1.0524053946556322,1000 +RF Regr.,RF Regr.,IV-type,0.95,0.917,0.15633394303010376,0.036530036698981375,1.0166818516109433,1.0524053946556322,1000 +RF Regr.,RF Regr.,partialling out,0.9,0.888,0.14202142008522262,0.03530584490921706,1.1540093754976348,1.0526197092895246,1000 +RF Regr.,RF Regr.,partialling out,0.95,0.937,0.16922895985350928,0.03530584490921706,1.1540093754976348,1.0526197092895246,1000 diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv index 7c4c7b6..f6e9b21 100644 --- a/results/plm/plr_ate_metadata.csv +++ b/results/plm/plr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLRATECoverageSimulation,2025-12-04 20:23,193.46772919098535,3.12.3,scripts/plm/plr_ate_config.yml +0.12.dev0,PLRATECoverageSimulation,2025-12-08 15:12,19.93783419529597,3.12.9,scripts/plm/plr_ate_config.yml From 7a31741b7d4bf4ab2e17eabe5cefbc4c86807edc Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 16 Jan 2026 10:33:24 +0100 Subject: [PATCH 4/5] rerun plpr simulations --- results/plm/plpr_ate_config.yml | 2 +- results/plm/plpr_ate_coverage.csv | 192 ++++++++++++------------- results/plm/plpr_ate_metadata.csv | 2 +- results/plm/plpr_ate_tune_config.yml | 2 +- results/plm/plpr_ate_tune_coverage.csv | 96 ++++++------- results/plm/plpr_ate_tune_metadata.csv | 2 +- scripts/plm/plpr_ate_config.yml | 2 +- scripts/plm/plpr_ate_tune_config.yml | 2 +- 8 files changed, 150 insertions(+), 150 deletions(-) diff --git a/results/plm/plpr_ate_config.yml b/results/plm/plpr_ate_config.yml index 7cb98fe..06e132c 100644 --- a/results/plm/plpr_ate_config.yml +++ b/results/plm/plpr_ate_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 200 + repetitions: 500 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/plm/plpr_ate_coverage.csv b/results/plm/plpr_ate_coverage.csv index 1a28083..8057e13 100644 --- a/results/plm/plpr_ate_coverage.csv +++ b/results/plm/plpr_ate_coverage.csv @@ -1,97 +1,97 @@ Learner g,Learner m,Score,Approach,DGP,level,Coverage,CI Length,Bias,Loss g,Loss m,repetition -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.9,0.685,0.07825958532713045,0.03043935901390476,1.5482634245331157,1.0388990792815624,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.95,0.795,0.09325204758218905,0.03043935901390476,1.5482634245331157,1.0388990792815624,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.9,0.9,0.07568235712288829,0.019227801904568253,1.5453227852715608,1.014488281057047,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.95,0.945,0.0901810907642153,0.019227801904568253,1.5453227852715608,1.014488281057047,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.9,0.0,0.09459012254838152,0.2898336465542013,1.6803016907278263,1.2538635096584279,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.95,0.0,0.11271108288927856,0.2898336465542013,1.6803016907278263,1.2538635096584279,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.9,0.405,0.0914507937941699,0.052651499081212826,1.5346644973650363,1.0715450800990285,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.95,0.55,0.1089703419546037,0.052651499081212826,1.5346644973650363,1.0715450800990285,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.9,0.875,0.09376358856769959,0.02562869058928203,1.5417071621936826,1.0365404860260867,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.95,0.935,0.11172620690542732,0.02562869058928203,1.5417071621936826,1.0365404860260867,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.9,0.0,0.10373060826155252,0.21990420865980828,1.7204436818144588,1.4734592546679581,200 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.95,0.0,0.1236026433937967,0.21990420865980828,1.7204436818144588,1.4734592546679581,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.9,0.475,0.0845922654157919,0.04556366892982161,1.5592571956779957,1.560381433558009,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.95,0.595,0.10079790132626604,0.04556366892982161,1.5592571956779957,1.560381433558009,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.9,0.85,0.0874896273640717,0.02440724171781883,1.503506135854519,1.51547345213435,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.95,0.92,0.10425032102839481,0.02440724171781883,1.503506135854519,1.51547345213435,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.9,0.0,0.08785625331373677,0.39048686946534206,1.8332923873454399,2.0339019268207594,200 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.95,0.0,0.10468718279248568,0.39048686946534206,1.8332923873454399,2.0339019268207594,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.9,0.755,0.0773198192870836,0.025688659094466836,1.0206898951802998,1.0208011908233934,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.95,0.825,0.09213224727764806,0.025688659094466836,1.0206898951802998,1.0208011908233934,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.9,0.9,0.06915096912963344,0.016904622144843975,1.0166398752165657,1.1183173279947765,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.95,0.96,0.08239846194783704,0.016904622144843975,1.0166398752165657,1.1183173279947765,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.9,0.0,0.0644883030727697,0.6668132448114582,1.3111193828356513,1.7845823260498657,200 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.95,0.0,0.07684255265983088,0.6668132448114582,1.3111193828356513,1.7845823260498657,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,0.88,0.07276301215662687,0.01883933964227799,1.827594776452371,1.0388491161653157,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,0.935,0.08670247668052608,0.01883933964227799,1.827594776452371,1.0388491161653157,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,0.53,0.07032087926180335,0.035930388703310105,1.8138875087628077,1.0140533010272026,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,0.62,0.08379249585251419,0.035930388703310105,1.8138875087628077,1.0140533010272026,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,0.0,0.14644793400955328,0.24387897456620083,2.2265021730931096,1.2509985001043957,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,0.0,0.1745034765196716,0.24387897456620083,2.2265021730931096,1.2509985001043957,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,0.34,0.10671440925189961,0.06604278972783294,1.8278662384821258,1.0723966288109512,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,0.475,0.1271580615673603,0.06604278972783294,1.8278662384821258,1.0723966288109512,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,0.885,0.09882487285553931,0.02624515366426892,1.814110699387459,1.0369175101803143,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,0.92,0.11775709911196953,0.02624515366426892,1.814110699387459,1.0369175101803143,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,0.16,0.1449329301123928,0.11835881606523448,2.227210435501958,1.472624525509306,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,0.24,0.17269823803144477,0.11835881606523448,2.227210435501958,1.472624525509306,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,0.755,0.08348771770764904,0.029398969242520855,1.7917213036062813,1.5606673925355516,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,0.835,0.09948175155360901,0.029398969242520855,1.7917213036062813,1.5606673925355516,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,0.64,0.08083241423212986,0.033634783973774104,1.685790636282,1.514365604383286,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,0.725,0.09631776231178987,0.033634783973774104,1.685790636282,1.514365604383286,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,0.0,0.13794149985824275,0.3445870684575309,2.6732063767531145,2.03525478663509,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,0.0,0.16436743505054094,0.3445870684575309,2.6732063767531145,2.03525478663509,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,0.875,0.070192708182761,0.01790301329858119,1.1521347825360824,1.0215954484316985,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,0.945,0.08363977059193994,0.01790301329858119,1.1521347825360824,1.0215954484316985,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,0.65,0.062346536560795245,0.024534437530928276,1.1597307643399963,1.1183251465249968,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,0.77,0.07429048045232146,0.024534437530928276,1.1597307643399963,1.1183251465249968,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,0.0,0.0757314775470122,0.634032650278728,2.495996501856889,1.7833391473200728,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,0.0,0.09023962135964987,0.634032650278728,2.495996501856889,1.7833391473200728,200 -LassoCV,LassoCV,IV-type,cre_general,dgp1,0.9,0.88,0.061984516414491944,0.016324731772937556,1.4342287909763354,0.9510592759499467,200 -LassoCV,LassoCV,IV-type,cre_general,dgp1,0.95,0.93,0.07385910684143827,0.016324731772937556,1.4342287909763354,0.9510592759499467,200 -LassoCV,LassoCV,IV-type,cre_general,dgp2,0.9,0.905,0.057620160966157796,0.013803833825012301,1.453044641353273,1.1603287550933408,200 -LassoCV,LassoCV,IV-type,cre_general,dgp2,0.95,0.95,0.06865865656774442,0.013803833825012301,1.453044641353273,1.1603287550933408,200 -LassoCV,LassoCV,IV-type,cre_general,dgp3,0.9,0.0,0.040695837684316956,0.8575431720219743,1.8204580665305374,2.5600132505674766,200 -LassoCV,LassoCV,IV-type,cre_general,dgp3,0.95,0.0,0.04849208152933258,0.8575431720219743,1.8204580665305374,2.5600132505674766,200 -LassoCV,LassoCV,IV-type,cre_normal,dgp1,0.9,0.9,0.06815492223630427,0.01670678701720667,1.4322233132712798,0.9501754225061405,200 -LassoCV,LassoCV,IV-type,cre_normal,dgp1,0.95,0.96,0.0812115988702656,0.01670678701720667,1.4322233132712798,0.9501754225061405,200 -LassoCV,LassoCV,IV-type,cre_normal,dgp2,0.9,0.89,0.056681208257812934,0.013940268110537163,1.4486204122627973,1.1611849557661176,200 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-LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.9,0.0,0.0472369281538713,0.8568418398893103,1.9590835619912503,3.813136954208584,200 -LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.95,0.0,0.05628627155930228,0.8568418398893103,1.9590835619912503,3.813136954208584,200 -LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.9,0.89,0.06553272978973308,0.01649413851419959,0.9495371672185499,0.9494711664131666,200 -LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.95,0.95,0.07808706385292322,0.01649413851419959,0.9495371672185499,0.9494711664131666,200 -LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.9,0.915,0.05763672107912186,0.013769696120827644,0.9686100757517228,1.160106460884591,200 -LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.95,0.955,0.06867838915942166,0.013769696120827644,0.9686100757517228,1.160106460884591,200 -LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.9,0.0,0.04058903382821993,0.8580755964454261,1.3122692024152842,2.5598958059389676,200 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out,fd_exact,dgp2,0.9,0.882,0.06957322998084599,0.01712617163896571,1.6879261548240179,1.7319843763207607,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.95,0.936,0.08290161678599228,0.01712617163896571,1.6879261548240179,1.7319843763207607,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.9,0.0,0.0482053987448878,0.8551692855820339,5.554065342016109,3.825142700788823,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.95,0.0,0.057440275445956056,0.8551692855820339,5.554065342016109,3.825142700788823,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.9,0.892,0.06896110455441337,0.016715083303867763,1.0634308913046913,0.9505881612839463,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.95,0.956,0.08217222435242194,0.016715083303867763,1.0634308913046913,0.9505881612839463,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.9,0.882,0.05761184132459587,0.014335705455119392,1.1306170872238346,1.1615635615855753,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.95,0.932,0.0686487431033738,0.014335705455119392,1.1306170872238346,1.1615635615855753,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.9,0.0,0.04115131704178429,0.8568060452016073,3.724261392351412,2.5643019957576962,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.95,0.0,0.049034818659074615,0.8568060452016073,3.724261392351412,2.5643019957576962,500 diff --git a/results/plm/plpr_ate_metadata.csv b/results/plm/plpr_ate_metadata.csv index 9a468cf..bb4fff9 100644 --- a/results/plm/plpr_ate_metadata.csv +++ b/results/plm/plpr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLPRATECoverageSimulation,2025-12-08 13:35,8.844934634367625,3.12.9,scripts/plm/plpr_ate_config.yml +0.11.2.dev96,PLPRATECoverageSimulation,2026-01-15 22:20,21.816437701384228,3.12.9,scripts/plm/plpr_ate_config.yml diff --git a/results/plm/plpr_ate_tune_config.yml b/results/plm/plpr_ate_tune_config.yml index e69a8e8..f938162 100644 --- a/results/plm/plpr_ate_tune_config.yml +++ b/results/plm/plpr_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/plm/plpr_ate_tune_coverage.csv b/results/plm/plpr_ate_tune_coverage.csv index 7981d57..21e7f58 100644 --- a/results/plm/plpr_ate_tune_coverage.csv +++ b/results/plm/plpr_ate_tune_coverage.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,Approach,DGP,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.84,0.07214938085352593,0.020958634872141193,1.8380925400044046,1.0425563302438159,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.84,0.07214938085352593,0.020958634872141193,1.8406447982984568,1.0536808246925673,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.87,0.08597128988423149,0.020958634872141193,1.8380925400044046,1.0425563302438159,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.87,0.08597128988423149,0.020958634872141193,1.8406447982984568,1.0536808246925673,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,False,0.56,0.07019217379642241,0.03223609623930599,1.8417454099789756,1.0114586592127623,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,True,0.56,0.07019217379642241,0.03223609623930599,1.845874406802228,1.0235401498732124,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,False,0.64,0.08363913383134296,0.03223609623930599,1.8417454099789756,1.0114586592127623,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,True,0.64,0.08363913383134296,0.03223609623930599,1.845874406802228,1.0235401498732124,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.14506683792132763,0.24426316655751223,2.2357614775306525,1.2646810617814317,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.0,0.14506683792132763,0.24426316655751223,2.2163775352398476,1.2616692581843845,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.17285779902730514,0.24426316655751223,2.2357614775306525,1.2646810617814317,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.0,0.17285779902730514,0.24426316655751223,2.2163775352398476,1.2616692581843845,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.37,0.10564657766155527,0.0639652478345845,1.8351842533954925,1.075452347488812,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.37,0.10564657766155527,0.0639652478345845,1.84118987710939,1.0701310013996495,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.48,0.12588566174750032,0.0639652478345845,1.8351842533954925,1.075452347488812,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.48,0.12588566174750032,0.0639652478345845,1.84118987710939,1.0701310013996495,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,False,0.9,0.09978560477366961,0.025742513859099113,1.8385051319239296,1.0355345852928854,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,True,0.9,0.09978560477366961,0.025742513859099113,1.8419745101738403,1.020917516314259,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,False,0.94,0.11890188180112794,0.025742513859099113,1.8385051319239296,1.0355345852928854,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,True,0.94,0.11890188180112794,0.025742513859099113,1.8419745101738403,1.020917516314259,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.21,0.14736942212785784,0.11116202396744745,2.2330986160573976,1.4764474809056063,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.21,0.14736942212785784,0.11116202396744745,2.234545448937938,1.4524951630618492,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.35,0.17560149733712627,0.11116202396744745,2.2330986160573976,1.4764474809056063,100 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.35,0.17560149733712627,0.11116202396744745,2.234545448937938,1.4524951630618492,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.75,0.08338178511761866,0.027758914202157547,1.7857580223977323,1.5617592387653514,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.75,0.08338178511761866,0.027758914202157547,1.7755675591454931,1.5446407468542838,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.88,0.09935552508710367,0.027758914202157547,1.7857580223977323,1.5617592387653514,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.88,0.09935552508710367,0.027758914202157547,1.7755675591454931,1.5446407468542838,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,False,0.75,0.08172180822747643,0.0288237206890361,1.6839348474894564,1.5107731514052591,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,True,0.75,0.08172180822747643,0.0288237206890361,1.6339834968196845,1.4811015397253107,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,False,0.81,0.0973775406229921,0.0288237206890361,1.6839348474894564,1.5107731514052591,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,True,0.81,0.0973775406229921,0.0288237206890361,1.6339834968196845,1.4811015397253107,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.13951582228199874,0.3374665458880539,2.6600546733155745,2.0403787961336346,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.13951582228199874,0.3374665458880539,2.6731472910158462,2.0516197504849605,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.16624335592280381,0.3374665458880539,2.6600546733155745,2.0403787961336346,100 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.16624335592280381,0.3374665458880539,2.6731472910158462,2.0516197504849605,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.87,0.06986442022400832,0.017559392666425425,1.154335223709233,1.0265129772476522,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.87,0.06986442022400832,0.017559392666425425,1.1343289505288514,1.0057938448034482,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.95,0.08324859136166037,0.017559392666425425,1.154335223709233,1.0265129772476522,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.95,0.08324859136166037,0.017559392666425425,1.1343289505288514,1.0057938448034482,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,False,0.63,0.06201367415287924,0.02529730149300308,1.1565802541819723,1.1157816130918814,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,True,0.63,0.06201367415287924,0.02529730149300308,1.1144561853005877,1.0769038366613766,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,False,0.73,0.07389385042966602,0.02529730149300308,1.1565802541819723,1.1157816130918814,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,True,0.73,0.07389385042966602,0.02529730149300308,1.1144561853005877,1.0769038366613766,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.07574491665108128,0.6306933353030978,2.494072876419393,1.7857864356531934,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.07574491665108128,0.6306933353030978,2.560473569307849,1.834203386940789,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.09025563503985116,0.6306933353030978,2.494072876419393,1.7857864356531934,100 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.09025563503985116,0.6306933353030978,2.560473569307849,1.834203386940789,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.89,0.07261917027364388,0.019235054197761097,1.8361787262583364,1.0404648655210367,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.89,0.07261917027364388,0.019235054197761097,1.8440047918328,1.0513028557786874,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.94,0.08653107850533552,0.019235054197761097,1.8361787262583364,1.0404648655210367,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.94,0.08653107850533552,0.019235054197761097,1.8440047918328,1.0513028557786874,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,False,0.6,0.0700152677964307,0.03198235111492452,1.8240864106139747,1.0151248226390528,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,True,0.6,0.0700152677964307,0.03198235111492452,1.8303518120277622,1.0253750664665986,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,False,0.69,0.08342833733069906,0.03198235111492452,1.8240864106139747,1.0151248226390528,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,True,0.69,0.08342833733069906,0.03198235111492452,1.8303518120277622,1.0253750664665986,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.1462163502282219,0.24567486215578818,2.2279933069499065,1.2505641228927882,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.0,0.1462163502282219,0.24567486215578818,2.222250935119112,1.2572923091327528,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.17422752742403425,0.24567486215578818,2.2279933069499065,1.2505641228927882,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.0,0.17422752742403425,0.24567486215578818,2.222250935119112,1.2572923091327528,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.355,0.10603858207543995,0.06489916106855874,1.8374578222083622,1.0735458838438718,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.355,0.10603858207543995,0.06489916106855874,1.842667271259383,1.0695053178941207,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.475,0.12635276381689156,0.06489916106855874,1.8374578222083622,1.0735458838438718,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.475,0.12635276381689156,0.06489916106855874,1.842667271259383,1.0695053178941207,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,False,0.915,0.09947368051992422,0.022277836350288207,1.8248827183224656,1.0385220092132057,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,True,0.915,0.09947368051992422,0.022277836350288207,1.8293718054414907,1.0238563379537278,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,False,0.965,0.11853020112801017,0.022277836350288207,1.8248827183224656,1.0385220092132057,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,True,0.965,0.11853020112801017,0.022277836350288207,1.8293718054414907,1.0238563379537278,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.14,0.1456697919504518,0.11380869099848887,2.229431291485762,1.4732977095693909,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.14,0.1456697919504518,0.11380869099848887,2.227865626320487,1.445298235347619,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.235,0.17357626306693333,0.11380869099848887,2.229431291485762,1.4732977095693909,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.235,0.17357626306693333,0.11380869099848887,2.227865626320487,1.445298235347619,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.75,0.08280385916225236,0.028686107344336492,1.789732218648727,1.5646363437982267,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.75,0.08280385916225236,0.028686107344336492,1.7768932970310254,1.5456576147179024,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.85,0.09866688383678877,0.028686107344336492,1.789732218648727,1.5646363437982267,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.85,0.09866688383678877,0.028686107344336492,1.7768932970310254,1.5456576147179024,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,False,0.705,0.08074359999671593,0.0318387032784245,1.6888041255737973,1.51878797250059,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,True,0.705,0.08074359999671593,0.0318387032784245,1.6401825056805919,1.4868034279511593,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,False,0.785,0.09621193362291841,0.0318387032784245,1.6888041255737973,1.51878797250059,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,True,0.785,0.09621193362291841,0.0318387032784245,1.6401825056805919,1.4868034279511593,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.13924492106829842,0.3399992035355032,2.6631585522839907,2.031731409601795,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.13924492106829842,0.3399992035355032,2.673299889854406,2.05218322369025,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.16592055721687587,0.3399992035355032,2.6631585522839907,2.031731409601795,200 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.16592055721687587,0.3399992035355032,2.673299889854406,2.05218322369025,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.87,0.06942711211214694,0.01876633539347498,1.1534314565687196,1.0238590256020452,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.87,0.06942711211214694,0.01876633539347498,1.1347790808297975,1.0040140440623913,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.94,0.08272750660654812,0.01876633539347498,1.1534314565687196,1.0238590256020452,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.94,0.08272750660654812,0.01876633539347498,1.1347790808297975,1.0040140440623913,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,False,0.695,0.062152399350612446,0.024051597450374388,1.1601279135717968,1.119614111898964,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,True,0.695,0.062152399350612446,0.024051597450374388,1.1166135931411847,1.0800167329397283,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,False,0.805,0.07405915169833223,0.024051597450374388,1.1601279135717968,1.119614111898964,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,True,0.805,0.07405915169833223,0.024051597450374388,1.1166135931411847,1.0800167329397283,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.07570291442887173,0.6331605054840328,2.4971967794668175,1.785265660424655,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.07570291442887173,0.6331605054840328,2.561461754242683,1.8300261872768113,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.09020558630514765,0.6331605054840328,2.4971967794668175,1.785265660424655,200 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.09020558630514765,0.6331605054840328,2.561461754242683,1.8300261872768113,200 diff --git a/results/plm/plpr_ate_tune_metadata.csv b/results/plm/plpr_ate_tune_metadata.csv index 9e9111a..7a80c6b 100644 --- a/results/plm/plpr_ate_tune_metadata.csv +++ b/results/plm/plpr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLPRATETuningCoverageSimulation,2025-12-08 14:50,62.522164789835614,3.12.9,scripts/plm/plpr_ate_tune_config.yml +0.11.2.dev96,PLPRATETuningCoverageSimulation,2026-01-16 09:42,123.22425887982051,3.12.9,scripts/plm/plpr_ate_tune_config.yml diff --git a/scripts/plm/plpr_ate_config.yml b/scripts/plm/plpr_ate_config.yml index dd1be50..7175b2f 100644 --- a/scripts/plm/plpr_ate_config.yml +++ b/scripts/plm/plpr_ate_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 200 + repetitions: 500 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 diff --git a/scripts/plm/plpr_ate_tune_config.yml b/scripts/plm/plpr_ate_tune_config.yml index a2f4e3d..8544cb2 100644 --- a/scripts/plm/plpr_ate_tune_config.yml +++ b/scripts/plm/plpr_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 From ee525f8b07a6699406a09bc02eb22654a7705da9 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 16 Jan 2026 17:56:49 +0100 Subject: [PATCH 5/5] Update PLR ATE simulation configurations and results - Adjusted the number of repetitions from 200 to 100 in tuning configuration. - Increased the number of units from 250 to 1000 in both main and tuning configurations. - Updated metadata files to reflect new runtime and simulation parameters. - Modified coverage results to include new entries based on updated configurations. --- doc/plm/plpr.qmd | 5 +- .../src/montecover/plm/plpr_ate_tune.py | 4 +- results/plm/plpr_ate_config.yml | 2 +- results/plm/plpr_ate_coverage.csv | 192 +++++++++--------- results/plm/plpr_ate_metadata.csv | 2 +- results/plm/plpr_ate_tune_config.yml | 5 +- results/plm/plpr_ate_tune_coverage.csv | 80 +++----- results/plm/plpr_ate_tune_metadata.csv | 2 +- scripts/plm/plpr_ate_config.yml | 2 +- scripts/plm/plpr_ate_tune_config.yml | 6 +- 10 files changed, 141 insertions(+), 159 deletions(-) diff --git a/doc/plm/plpr.qmd b/doc/plm/plpr.qmd index 3bfbc61..f2159dd 100644 --- a/doc/plm/plpr.qmd +++ b/doc/plm/plpr.qmd @@ -25,7 +25,7 @@ init_notebook_mode(all_interactive=True) ## Coverage -The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered: +The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $1000$ units and $10$ time periods. The following DGPs are considered: - DGP 1: Linear in the nuisance parameters - DGP 2: Non-linear and smooth in the nuisance parameters @@ -122,10 +122,9 @@ generate_and_show_styled_table( ## Tuning -The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered: +The simulations are based on the the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $1000$ units and $10$ time periods. The following DGPs are considered: - DGP 1: Linear in the nuisance parameters - - DGP 2: Non-linear and smooth in the nuisance parameters - DGP 3: Non-linear and discontinuous in the nuisance parameters This is only an example as the untuned version just relies on the default configuration. diff --git a/monte-cover/src/montecover/plm/plpr_ate_tune.py b/monte-cover/src/montecover/plm/plpr_ate_tune.py index 041a284..dde0230 100644 --- a/monte-cover/src/montecover/plm/plpr_ate_tune.py +++ b/monte-cover/src/montecover/plm/plpr_ate_tune.py @@ -98,9 +98,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: for level in self.confidence_parameters["level"]: level_result = dict() level_result["coverage"] = self._compute_coverage( - thetas=dml_model.coef, + thetas=model.coef, oracle_thetas=self.oracle_values["theta"], - confint=dml_model.confint(level=level), + confint=model.confint(level=level), joint_confint=None, ) diff --git a/results/plm/plpr_ate_config.yml b/results/plm/plpr_ate_config.yml index 06e132c..4ecda65 100644 --- a/results/plm/plpr_ate_config.yml +++ b/results/plm/plpr_ate_config.yml @@ -9,7 +9,7 @@ dgp_parameters: dim_x: - 10 num_id: - - 250 + - 1000 num_t: - 10 DGP: diff --git a/results/plm/plpr_ate_coverage.csv b/results/plm/plpr_ate_coverage.csv index 8057e13..93eade8 100644 --- a/results/plm/plpr_ate_coverage.csv +++ b/results/plm/plpr_ate_coverage.csv @@ -1,97 +1,97 @@ Learner g,Learner m,Score,Approach,DGP,level,Coverage,CI Length,Bias,Loss g,Loss m,repetition -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.9,0.684,0.07819084588175022,0.030150682912822104,1.5555201487882908,1.0409130260483672,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.95,0.79,0.09317013948103348,0.030150682912822104,1.5555201487882908,1.0409130260483672,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.9,0.888,0.07556309965255072,0.018230434031185072,1.5480725372090374,1.0143440897411475,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.95,0.938,0.09003898672351583,0.018230434031185072,1.5480725372090374,1.0143440897411475,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.9,0.0,0.09407125309439754,0.2866634575993452,1.6860151213356493,1.2566482938582006,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.95,0.0,0.11209281180070067,0.2866634575993452,1.6860151213356493,1.2566482938582006,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.9,0.386,0.09148965285802313,0.054262254334023714,1.542118951434373,1.0734947090849818,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.95,0.514,0.10901664538512015,0.054262254334023714,1.542118951434373,1.0734947090849818,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.9,0.888,0.09369206685349664,0.024253766594152012,1.5410510096708272,1.0383667595329291,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.95,0.934,0.11164098352648744,0.024253766594152012,1.5410510096708272,1.0383667595329291,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.9,0.0,0.10359119486318807,0.21082808227338257,1.72407241162174,1.4717399367299169,500 -LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.95,0.0,0.12343652208349928,0.21082808227338257,1.72407241162174,1.4717399367299169,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.9,0.474,0.0844960955192811,0.046058207979973335,1.5608551291700767,1.5618841891906705,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.95,0.588,0.10068330782659551,0.046058207979973335,1.5608551291700767,1.5618841891906705,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.9,0.874,0.08737263634328063,0.02355123658313944,1.5036804669798676,1.5179510521955546,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.95,0.92,0.10411091762890201,0.02355123658313944,1.5036804669798676,1.5179510521955546,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.9,0.0,0.08877944859962378,0.3887088265625147,1.8386718096562824,2.040664568534615,500 -LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.95,0.0,0.10578723782558262,0.3887088265625147,1.8386718096562824,2.040664568534615,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.9,0.8,0.07644562866963682,0.02515634199087879,1.0236768681826391,1.0227106596016102,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.95,0.874,0.09109058490858118,0.02515634199087879,1.0236768681826391,1.0227106596016102,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.9,0.882,0.06888055136675604,0.017248049797270796,1.016536103434304,1.1199152760580928,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.95,0.93,0.08207623930909569,0.017248049797270796,1.016536103434304,1.1199152760580928,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.9,0.0,0.06399191895807169,0.667076308516841,1.3123864711095206,1.7842997951029316,500 -LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.95,0.0,0.07625107450556552,0.667076308516841,1.3123864711095206,1.7842997951029316,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,0.858,0.07259892298875865,0.019758252631448134,1.8371353738618945,1.0412663844375385,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,0.918,0.08650695237732661,0.019758252631448134,1.8371353738618945,1.0412663844375385,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,0.538,0.07003698870488045,0.03438247750287817,1.8190119585461355,1.0148219324853067,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,0.672,0.08345421938948863,0.03438247750287817,1.8190119585461355,1.0148219324853067,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,0.0,0.14649545966695107,0.2413977401607792,2.2277828553427046,1.2558904775050652,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,0.0,0.1745601068333451,0.2413977401607792,2.2277828553427046,1.2558904775050652,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,0.366,0.1056261934463508,0.06449735239835869,1.8370621797225712,1.0730187322949067,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,0.492,0.1258613724569524,0.06449735239835869,1.8370621797225712,1.0730187322949067,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,0.866,0.09913149490977227,0.024581418745972975,1.8211265816757267,1.038110750782551,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,0.944,0.11812246182468464,0.024581418745972975,1.8211265816757267,1.038110750782551,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,0.236,0.14718563377922356,0.10762344982577529,2.2246447799225257,1.4729581499082038,500 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,0.342,0.17538250001225858,0.10762344982577529,2.2246447799225257,1.4729581499082038,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,0.716,0.08336190084312087,0.030234336489105732,1.7916580951078354,1.5620795633410112,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,0.806,0.09933183151265071,0.030234336489105732,1.7916580951078354,1.5620795633410112,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,0.712,0.08102099176947461,0.030805937503034682,1.6893366699122085,1.5173596248121854,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,0.784,0.0965424663069861,0.030805937503034682,1.6893366699122085,1.5173596248121854,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,0.0,0.13957647114321758,0.33820432994334354,2.662329212687004,2.0388703485774946,500 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,0.0,0.16631562349831602,0.33820432994334354,2.662329212687004,2.0388703485774946,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,0.874,0.06995874248820289,0.01872527491436724,1.155801237640369,1.0228916138453372,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,0.926,0.08336098327163494,0.01872527491436724,1.155801237640369,1.0228916138453372,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,0.672,0.06221201254446818,0.02481252374309639,1.1605791835879995,1.1203034719091638,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,0.756,0.07413018520006524,0.02481252374309639,1.1605791835879995,1.1203034719091638,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,0.0,0.07465980531744841,0.6330162870906783,2.4950262376144727,1.7843148820552728,500 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,0.0,0.08896264513589335,0.6330162870906783,2.4950262376144727,1.7843148820552728,500 -LassoCV,LassoCV,IV-type,cre_general,dgp1,0.9,0.856,0.06197551962112464,0.01670456447117368,1.4367112157601851,0.9519796299942244,500 -LassoCV,LassoCV,IV-type,cre_general,dgp1,0.95,0.918,0.07384838650092446,0.01670456447117368,1.4367112157601851,0.9519796299942244,500 -LassoCV,LassoCV,IV-type,cre_general,dgp2,0.9,0.88,0.05759007109444643,0.014352588072176162,1.4509857239580808,1.16169982113909,500 -LassoCV,LassoCV,IV-type,cre_general,dgp2,0.95,0.934,0.06862280227415417,0.014352588072176162,1.4509857239580808,1.16169982113909,500 -LassoCV,LassoCV,IV-type,cre_general,dgp3,0.9,0.0,0.04047609854978462,0.8579756354032785,1.8266450693769616,2.5641567971238457,500 -LassoCV,LassoCV,IV-type,cre_general,dgp3,0.95,0.0,0.04823024620087504,0.8579756354032785,1.8266450693769616,2.5641567971238457,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp1,0.9,0.894,0.06771507118393694,0.016548915969986644,1.4341759548669741,0.9513467512786797,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp1,0.95,0.95,0.08068748401464786,0.016548915969986644,1.4341759548669741,0.9513467512786797,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp2,0.9,0.858,0.05666559584819076,0.015251923010405387,1.4468219283184787,1.1623811257198566,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp2,0.95,0.914,0.06752122207420769,0.015251923010405387,1.4468219283184787,1.1623811257198566,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp3,0.9,0.0,0.04060328668193641,0.8541846299550794,1.8298139428016693,2.566348165579246,500 -LassoCV,LassoCV,IV-type,cre_normal,dgp3,0.95,0.0,0.04838180020798779,0.8541846299550794,1.8298139428016693,2.566348165579246,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.9,0.834,0.07680923675244254,0.0212769921235102,1.4198777145010015,1.4193366643307472,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.95,0.91,0.09152385066251194,0.0212769921235102,1.4198777145010015,1.4193366643307472,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.9,0.882,0.069590964432338,0.017179154493115717,1.4464059417304131,1.731943762607489,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.95,0.94,0.08292274868833324,0.017179154493115717,1.4464059417304131,1.731943762607489,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.9,0.0,0.04703496679887459,0.8578258529697558,1.9628045108542005,3.825374778322359,500 -LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.95,0.0,0.056045619761734077,0.8578258529697558,1.9628045108542005,3.825374778322359,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.9,0.876,0.06511787510979346,0.01672535653797427,0.9509161186481234,0.9506076167576101,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.95,0.942,0.07759273401215402,0.01672535653797427,0.9509161186481234,0.9506076167576101,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.9,0.878,0.057640134039266704,0.014332696534036671,0.9689372005570028,1.1615345954859186,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.95,0.934,0.06868245595226874,0.014332696534036671,0.9689372005570028,1.1615345954859186,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.9,0.0,0.04038684459006718,0.8584785275624095,1.3137353917006,2.5641258352417546,500 -LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.95,0.0,0.04812389354817853,0.8584785275624095,1.3137353917006,2.5641258352417546,500 -LassoCV,LassoCV,partialling out,cre_general,dgp1,0.9,0.894,0.06889082273433537,0.016744196099545437,1.6900574546023945,0.9519872844801529,500 -LassoCV,LassoCV,partialling out,cre_general,dgp1,0.95,0.954,0.08208847839845755,0.016744196099545437,1.6900574546023945,0.9519872844801529,500 -LassoCV,LassoCV,partialling out,cre_general,dgp2,0.9,0.882,0.05761240393620928,0.014421252502519306,1.7429410907133474,1.161682374314478,500 -LassoCV,LassoCV,partialling out,cre_general,dgp2,0.95,0.934,0.06864941349646016,0.014421252502519306,1.7429410907133474,1.161682374314478,500 -LassoCV,LassoCV,partialling out,cre_general,dgp3,0.9,0.0,0.04106996708764687,0.8569707418977986,4.190670651884921,2.5638229693902983,500 -LassoCV,LassoCV,partialling out,cre_general,dgp3,0.95,0.0,0.0489378842099292,0.8569707418977986,4.190670651884921,2.5638229693902983,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.9,0.862,0.0692697586987601,0.018599042983027284,1.6900882424799557,0.9513300828791466,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.95,0.928,0.08254000844985544,0.018599042983027284,1.6900882424799557,0.9513300828791466,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.9,0.69,0.058410955570082614,0.02243723379597576,1.7427880110176783,1.162421656824225,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.95,0.774,0.0696009464575347,0.02243723379597576,1.7427880110176783,1.162421656824225,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.9,0.0,0.04122821497717597,0.870384118496527,4.191146794330971,2.5664653864504725,500 -LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.95,0.0,0.04912644820068463,0.870384118496527,4.191146794330971,2.5664653864504725,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.9,0.866,0.08298930782936742,0.021262333075842944,1.5879747976053302,1.419256986392538,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.95,0.944,0.09888785955315094,0.021262333075842944,1.5879747976053302,1.419256986392538,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.9,0.882,0.06957322998084599,0.01712617163896571,1.6879261548240179,1.7319843763207607,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.95,0.936,0.08290161678599228,0.01712617163896571,1.6879261548240179,1.7319843763207607,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.9,0.0,0.0482053987448878,0.8551692855820339,5.554065342016109,3.825142700788823,500 -LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.95,0.0,0.057440275445956056,0.8551692855820339,5.554065342016109,3.825142700788823,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.9,0.892,0.06896110455441337,0.016715083303867763,1.0634308913046913,0.9505881612839463,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.95,0.956,0.08217222435242194,0.016715083303867763,1.0634308913046913,0.9505881612839463,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.9,0.882,0.05761184132459587,0.014335705455119392,1.1306170872238346,1.1615635615855753,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.95,0.932,0.0686487431033738,0.014335705455119392,1.1306170872238346,1.1615635615855753,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.9,0.0,0.04115131704178429,0.8568060452016073,3.724261392351412,2.5643019957576962,500 -LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.95,0.0,0.049034818659074615,0.8568060452016073,3.724261392351412,2.5643019957576962,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.9,0.76,0.03514583618402707,0.012086933097448243,1.4920664942490383,0.9826812820928352,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp1,0.95,0.822,0.04187884684603002,0.012086933097448243,1.4920664942490383,0.9826812820928352,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.9,0.902,0.0352112355264963,0.00848052574435656,1.4868097497063664,0.9728827523238984,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp2,0.95,0.942,0.0419567749690873,0.00848052574435656,1.4868097497063664,0.9728827523238984,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.9,0.0,0.04310727857260173,0.14671677416272066,1.571773745295489,1.0622433672042766,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_general,dgp3,0.95,0.0,0.05136549057585367,0.14671677416272066,1.571773745295489,1.0622433672042766,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.9,0.232,0.038406550228071955,0.02742550727028207,1.483764048404902,0.999212223907986,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp1,0.95,0.364,0.04576422727471694,0.02742550727028207,1.483764048404902,0.999212223907986,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.9,0.86,0.04234693897695462,0.011253246849524612,1.4856533799380605,0.9964954831115506,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp2,0.95,0.924,0.05045949006670804,0.011253246849524612,1.4856533799380605,0.9964954831115506,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.9,0.0,0.049156980333193705,0.10411713452491797,1.5940307485754293,1.2923084248152257,500 +LGBM Regr.,LGBM Regr.,IV-type,cre_normal,dgp3,0.95,0.0,0.05857415484462794,0.10411713452491797,1.5940307485754293,1.2923084248152257,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.9,0.62,0.04102050808679343,0.017937560761299848,1.4771745005369754,1.4770380880496619,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp1,0.95,0.732,0.0488789501754378,0.017937560761299848,1.4771745005369754,1.4770380880496619,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.9,0.882,0.04190905372606799,0.010488386999141841,1.4488914582957235,1.4558939353569655,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp2,0.95,0.934,0.04993771760803054,0.010488386999141841,1.4488914582957235,1.4558939353569655,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.9,0.0,0.04370250031217863,0.1707879261368483,1.6051273815372227,1.632153918330209,500 +LGBM Regr.,LGBM Regr.,IV-type,fd_exact,dgp3,0.95,0.0,0.052074741024204015,0.1707879261368483,1.6051273815372227,1.632153918330209,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.9,0.86,0.03552645391101902,0.009773574848443971,0.975237573251918,0.9746874056099604,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp1,0.95,0.91,0.0423323808411274,0.009773574848443971,0.975237573251918,0.9746874056099604,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.9,0.9,0.03215088012049884,0.00768267021100153,0.9766529183354105,1.0740756727445167,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp2,0.95,0.94,0.03831013658293233,0.00768267021100153,0.9766529183354105,1.0740756727445167,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.9,0.0,0.03145360311737441,0.6525229675042551,1.254590697442093,1.6629745312688475,500 +LGBM Regr.,LGBM Regr.,IV-type,wg_approx,dgp3,0.95,0.0,0.03747927978754392,0.6525229675042551,1.254590697442093,1.6629745312688475,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,0.884,0.035521703515894315,0.009263409558316846,1.7589959457732671,0.9827241499397525,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,0.934,0.04232672039620745,0.009263409558316846,1.7589959457732671,0.9827241499397525,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,0.638,0.0348786728934553,0.01503203017611916,1.7526622862628904,0.9728084396340184,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,0.764,0.0415605021502273,0.01503203017611916,1.7526622862628904,0.9728084396340184,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,0.0,0.06598788253706468,0.13048470795365294,1.9493882632563304,1.0624147992608393,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,0.0,0.0786294118026844,0.13048470795365294,1.9493882632563304,1.0624147992608393,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,0.092,0.0450048480961515,0.03780612634140066,1.7583565503328924,0.9991400420492937,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,0.172,0.05362658412447015,0.03780612634140066,1.7583565503328924,0.9991400420492937,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,0.902,0.04599869759031349,0.011016558307233774,1.7513679581585158,0.996358190008618,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,0.94,0.05481082883943669,0.011016558307233774,1.7513679581585158,0.996358190008618,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,0.426,0.06931454796040375,0.04004134564072659,1.9470478710807493,1.2921443410214415,500 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,0.546,0.08259337814687731,0.04004134564072659,1.9470478710807493,1.2921443410214415,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,0.868,0.04160542943340649,0.010766955606101051,1.6715896763026044,1.4773030644679437,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,0.93,0.04957592694854741,0.010766955606101051,1.6715896763026044,1.4773030644679437,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,0.706,0.04117746823251659,0.015444730320919184,1.6238667435533736,1.4555522656672797,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,0.818,0.04906597972480611,0.015444730320919184,1.6238667435533736,1.4555522656672797,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,0.0,0.06904324805844071,0.15128321194444963,1.9891080939323569,1.6328417545770315,500 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,0.0,0.08227010437458288,0.15128321194444963,1.9891080939323569,1.6328417545770315,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,0.872,0.03490468627660247,0.009087617206985168,1.094306952229372,0.9748184939426158,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,0.942,0.041591499008092996,0.009087617206985168,1.094306952229372,0.9748184939426158,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,0.716,0.03126496342246468,0.011800053579619845,1.1147066793312614,1.0741359418425813,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,0.812,0.03725450172704079,0.011800053579619845,1.1147066793312614,1.0741359418425813,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,0.0,0.036138092388723404,0.6322847737379714,2.3054834813847025,1.6628430322181407,500 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,0.0,0.04306119303949992,0.6322847737379714,2.3054834813847025,1.6628430322181407,500 +LassoCV,LassoCV,IV-type,cre_general,dgp1,0.9,0.896,0.03276473125839177,0.00821022405330175,1.4246934279489174,0.9489026976001401,500 +LassoCV,LassoCV,IV-type,cre_general,dgp1,0.95,0.936,0.03904158532853829,0.00821022405330175,1.4246934279489174,0.9489026976001401,500 +LassoCV,LassoCV,IV-type,cre_general,dgp2,0.9,0.89,0.028861438302812854,0.007018329498324938,1.4393292657687904,1.1603587931918957,500 +LassoCV,LassoCV,IV-type,cre_general,dgp2,0.95,0.942,0.03439052490061289,0.007018329498324938,1.4393292657687904,1.1603587931918957,500 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+LassoCV,LassoCV,IV-type,cre_normal,dgp3,0.95,0.0,0.024213773642151538,0.8575641005497477,1.8154846352374259,2.5604995833015938,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.9,0.894,0.0399796161499072,0.009748362312755216,1.4151388592061662,1.414574320153688,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp1,0.95,0.936,0.047638650932595,0.009748362312755216,1.4151388592061662,1.414574320153688,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.9,0.89,0.03472218740166541,0.008339407196285804,1.442296972916924,1.7298507312867564,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp2,0.95,0.944,0.04137403818590503,0.008339407196285804,1.442296972916924,1.7298507312867564,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.9,0.0,0.023598130929631075,0.8587681734465551,1.9559180380947834,3.8155575850577184,500 +LassoCV,LassoCV,IV-type,fd_exact,dgp3,0.95,0.0,0.028118907340259135,0.8587681734465551,1.9559180380947834,3.8155575850577184,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.9,0.904,0.033671023532489415,0.008213981918648031,0.9490845712902075,0.9485363031047026,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp1,0.95,0.942,0.040121499180806375,0.008213981918648031,0.9490845712902075,0.9485363031047026,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.9,0.894,0.02886848144391098,0.007003403091165492,0.9672907575243995,1.1602428664343556,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp2,0.95,0.946,0.034398917320864864,0.007003403091165492,0.9672907575243995,1.1602428664343556,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.9,0.0,0.020294054418133808,0.8587746000766776,1.3121259699415952,2.5596321343060358,500 +LassoCV,LassoCV,IV-type,wg_approx,dgp3,0.95,0.0,0.024181857344691086,0.8587746000766776,1.3121259699415952,2.5596321343060358,500 +LassoCV,LassoCV,partialling out,cre_general,dgp1,0.9,0.91,0.03467943098074278,0.008251240800288466,1.6750736379383766,0.9489001592191306,500 +LassoCV,LassoCV,partialling out,cre_general,dgp1,0.95,0.952,0.0413230907680053,0.008251240800288466,1.6750736379383766,0.9489001592191306,500 +LassoCV,LassoCV,partialling out,cre_general,dgp2,0.9,0.89,0.028881863703602084,0.0070518376131271324,1.7287268520665562,1.1603520510978118,500 +LassoCV,LassoCV,partialling out,cre_general,dgp2,0.95,0.944,0.034414863266812,0.0070518376131271324,1.7287268520665562,1.1603520510978118,500 +LassoCV,LassoCV,partialling out,cre_general,dgp3,0.9,0.0,0.020370479530043516,0.8585524814448288,4.176721826273227,2.559726882415026,500 +LassoCV,LassoCV,partialling out,cre_general,dgp3,0.95,0.0,0.02427292348237234,0.8585524814448288,4.176721826273227,2.559726882415026,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.9,0.806,0.03478280442865251,0.010499227810699951,1.6752650843495283,0.9487438547123895,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp1,0.95,0.89,0.041446267828590504,0.010499227810699951,1.6752650843495283,0.9487438547123895,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.9,0.804,0.0289631665115877,0.009127066587909956,1.7289940040116214,1.1605626902587372,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp2,0.95,0.856,0.03451174153785252,0.009127066587909956,1.7289940040116214,1.1605626902587372,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.9,0.0,0.02035309555522939,0.8624902127509477,4.176610937542443,2.560480949006136,500 +LassoCV,LassoCV,partialling out,cre_normal,dgp3,0.95,0.0,0.024252209198751252,0.8624902127509477,4.176610937542443,2.560480949006136,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.9,0.904,0.041693471947824016,0.009706029679925616,1.5821336249148614,1.4145383386834307,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp1,0.95,0.956,0.04968083607513426,0.009706029679925616,1.5821336249148614,1.4145383386834307,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.9,0.896,0.03473840962559508,0.008340460416278743,1.682105933852392,1.7298235340863823,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp2,0.95,0.944,0.04139336815796475,0.008340460416278743,1.682105933852392,1.7298235340863823,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.9,0.0,0.02374608866600501,0.8586899397478316,5.543468443960648,3.8154775099637335,500 +LassoCV,LassoCV,partialling out,fd_exact,dgp3,0.95,0.0,0.028295209857258443,0.8586899397478316,5.543468443960648,3.8154775099637335,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.9,0.91,0.03469221899156352,0.008229913285302625,1.0609031375206512,0.9485499616068327,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp1,0.95,0.95,0.04133832862274926,0.008229913285302625,1.0609031375206512,0.9485499616068327,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.9,0.892,0.028882696610382973,0.007030114956002142,1.1279298355369647,1.1602246284252973,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp2,0.95,0.946,0.03441585573645566,0.007030114956002142,1.1279298355369647,1.1602246284252973,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.9,0.0,0.02037307527005654,0.8587768373127108,3.71824421002822,2.5596448613420026,500 +LassoCV,LassoCV,partialling out,wg_approx,dgp3,0.95,0.0,0.024276016497370993,0.8587768373127108,3.71824421002822,2.5596448613420026,500 diff --git a/results/plm/plpr_ate_metadata.csv b/results/plm/plpr_ate_metadata.csv index bb4fff9..e8b0af6 100644 --- a/results/plm/plpr_ate_metadata.csv +++ b/results/plm/plpr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.2.dev96,PLPRATECoverageSimulation,2026-01-15 22:20,21.816437701384228,3.12.9,scripts/plm/plpr_ate_config.yml +0.11.2.dev96,PLPRATECoverageSimulation,2026-01-16 15:02,36.614749721686046,3.12.9,scripts/plm/plpr_ate_config.yml diff --git a/results/plm/plpr_ate_tune_config.yml b/results/plm/plpr_ate_tune_config.yml index f938162..40ecc47 100644 --- a/results/plm/plpr_ate_tune_config.yml +++ b/results/plm/plpr_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 200 + repetitions: 100 max_runtime: 19800 random_seed: 42 n_jobs: -2 @@ -9,12 +9,11 @@ dgp_parameters: dim_x: - 10 num_id: - - 250 + - 1000 num_t: - 10 DGP: - dgp1 - - dgp2 - dgp3 learner_definitions: lgbm: &id001 diff --git a/results/plm/plpr_ate_tune_coverage.csv b/results/plm/plpr_ate_tune_coverage.csv index 21e7f58..d2cbb9d 100644 --- a/results/plm/plpr_ate_tune_coverage.csv +++ b/results/plm/plpr_ate_tune_coverage.csv @@ -1,49 +1,33 @@ Learner g,Learner m,Score,Approach,DGP,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.89,0.07261917027364388,0.019235054197761097,1.8361787262583364,1.0404648655210367,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.89,0.07261917027364388,0.019235054197761097,1.8440047918328,1.0513028557786874,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.94,0.08653107850533552,0.019235054197761097,1.8361787262583364,1.0404648655210367,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.94,0.08653107850533552,0.019235054197761097,1.8440047918328,1.0513028557786874,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,False,0.6,0.0700152677964307,0.03198235111492452,1.8240864106139747,1.0151248226390528,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,True,0.6,0.0700152677964307,0.03198235111492452,1.8303518120277622,1.0253750664665986,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,False,0.69,0.08342833733069906,0.03198235111492452,1.8240864106139747,1.0151248226390528,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,True,0.69,0.08342833733069906,0.03198235111492452,1.8303518120277622,1.0253750664665986,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.1462163502282219,0.24567486215578818,2.2279933069499065,1.2505641228927882,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.0,0.1462163502282219,0.24567486215578818,2.222250935119112,1.2572923091327528,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.17422752742403425,0.24567486215578818,2.2279933069499065,1.2505641228927882,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.0,0.17422752742403425,0.24567486215578818,2.222250935119112,1.2572923091327528,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.355,0.10603858207543995,0.06489916106855874,1.8374578222083622,1.0735458838438718,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.355,0.10603858207543995,0.06489916106855874,1.842667271259383,1.0695053178941207,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.475,0.12635276381689156,0.06489916106855874,1.8374578222083622,1.0735458838438718,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.475,0.12635276381689156,0.06489916106855874,1.842667271259383,1.0695053178941207,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,False,0.915,0.09947368051992422,0.022277836350288207,1.8248827183224656,1.0385220092132057,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,True,0.915,0.09947368051992422,0.022277836350288207,1.8293718054414907,1.0238563379537278,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,False,0.965,0.11853020112801017,0.022277836350288207,1.8248827183224656,1.0385220092132057,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,True,0.965,0.11853020112801017,0.022277836350288207,1.8293718054414907,1.0238563379537278,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.14,0.1456697919504518,0.11380869099848887,2.229431291485762,1.4732977095693909,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.14,0.1456697919504518,0.11380869099848887,2.227865626320487,1.445298235347619,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.235,0.17357626306693333,0.11380869099848887,2.229431291485762,1.4732977095693909,200 -LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.235,0.17357626306693333,0.11380869099848887,2.227865626320487,1.445298235347619,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.75,0.08280385916225236,0.028686107344336492,1.789732218648727,1.5646363437982267,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.75,0.08280385916225236,0.028686107344336492,1.7768932970310254,1.5456576147179024,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.85,0.09866688383678877,0.028686107344336492,1.789732218648727,1.5646363437982267,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.85,0.09866688383678877,0.028686107344336492,1.7768932970310254,1.5456576147179024,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,False,0.705,0.08074359999671593,0.0318387032784245,1.6888041255737973,1.51878797250059,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,True,0.705,0.08074359999671593,0.0318387032784245,1.6401825056805919,1.4868034279511593,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,False,0.785,0.09621193362291841,0.0318387032784245,1.6888041255737973,1.51878797250059,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,True,0.785,0.09621193362291841,0.0318387032784245,1.6401825056805919,1.4868034279511593,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.13924492106829842,0.3399992035355032,2.6631585522839907,2.031731409601795,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.13924492106829842,0.3399992035355032,2.673299889854406,2.05218322369025,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.16592055721687587,0.3399992035355032,2.6631585522839907,2.031731409601795,200 -LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.16592055721687587,0.3399992035355032,2.673299889854406,2.05218322369025,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.87,0.06942711211214694,0.01876633539347498,1.1534314565687196,1.0238590256020452,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.87,0.06942711211214694,0.01876633539347498,1.1347790808297975,1.0040140440623913,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.94,0.08272750660654812,0.01876633539347498,1.1534314565687196,1.0238590256020452,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.94,0.08272750660654812,0.01876633539347498,1.1347790808297975,1.0040140440623913,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,False,0.695,0.062152399350612446,0.024051597450374388,1.1601279135717968,1.119614111898964,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,True,0.695,0.062152399350612446,0.024051597450374388,1.1166135931411847,1.0800167329397283,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,False,0.805,0.07405915169833223,0.024051597450374388,1.1601279135717968,1.119614111898964,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,True,0.805,0.07405915169833223,0.024051597450374388,1.1166135931411847,1.0800167329397283,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.07570291442887173,0.6331605054840328,2.4971967794668175,1.785265660424655,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.07570291442887173,0.6331605054840328,2.561461754242683,1.8300261872768113,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.09020558630514765,0.6331605054840328,2.4971967794668175,1.785265660424655,200 -LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.09020558630514765,0.6331605054840328,2.561461754242683,1.8300261872768113,200 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.85,0.03536060813686728,0.010137837243259291,1.759385900869758,0.9829393756223068,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.43,0.03545698050140429,0.019649649061510455,1.7952996538071346,1.0074865255650423,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.88,0.042134763412439845,0.010137837243259291,1.759385900869758,0.9829393756223068,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.6,0.04224959816764391,0.019649649061510455,1.7952996538071346,1.0074865255650423,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.06858490422345778,0.13496018593341227,1.9519737434421347,1.065153140737538,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.02,0.06394880355115651,0.06224369604076415,1.9303888300716567,1.0663837612950566,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.08172395400935707,0.13496018593341227,1.9519737434421347,1.065153140737538,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.11,0.07619969932947211,0.06224369604076415,1.9303888300716567,1.0663837612950566,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.13,0.04499659790967875,0.037967155486427345,1.7597864638844198,1.000259350721019,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.05,0.04578777654320108,0.05361966770305959,1.7958356986277106,0.9953445685695448,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.19,0.053616753420942846,0.037967155486427345,1.7597864638844198,1.000259350721019,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.06,0.05455950091022274,0.05361966770305959,1.7958356986277106,0.9953445685695448,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.38,0.07033182725843065,0.0414062764743804,1.9511557372062653,1.292533000356088,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.75,0.07270339833567722,0.024516389764608012,1.9331219261429609,1.2700435982351912,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.5,0.08380554119511537,0.0414062764743804,1.9511557372062653,1.292533000356088,100 +LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.85,0.08663144243156458,0.024516389764608012,1.9331219261429609,1.2700435982351912,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.9,0.04138210865225103,0.010006694800155022,1.6707591787769969,1.477648072202423,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.85,0.041322602449946635,0.011232911566422777,1.6567152207263192,1.467277534675784,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.94,0.04930982382490638,0.010006694800155022,1.6707591787769969,1.477648072202423,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.92,0.04923891781147008,0.011232911566422777,1.6567152207263192,1.467277534675784,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.06993795589874738,0.15347152370108225,1.9933735415428915,1.6344498070427307,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.06974766121306378,0.08962925447476038,1.9625374204301658,1.642912272067501,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.0833362145225945,0.15347152370108225,1.9933735415428915,1.6344498070427307,100 +LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.08310946441895123,0.08962925447476038,1.9625374204301658,1.642912272067501,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.84,0.03477046525415805,0.00980255121555226,1.0936318545403032,0.9752601991470606,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.86,0.03476589854784296,0.009227874716762542,1.0883774496861216,0.9693908447489228,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.89,0.04143156479531641,0.00980255121555226,1.0936318545403032,0.9752601991470606,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.93,0.04142612322911326,0.009227874716762542,1.0883774496861216,0.9693908447489228,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.03627472064440453,0.6316795367533637,2.3058431372679435,1.6644837881054577,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.03648420793166297,0.6110007119820693,2.364071320754483,1.7088324600649871,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.043223995647596956,0.6316795367533637,2.3058431372679435,1.6644837881054577,100 +LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.04347361514657118,0.6110007119820693,2.364071320754483,1.7088324600649871,100 diff --git a/results/plm/plpr_ate_tune_metadata.csv b/results/plm/plpr_ate_tune_metadata.csv index 7a80c6b..5795390 100644 --- a/results/plm/plpr_ate_tune_metadata.csv +++ b/results/plm/plpr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.2.dev96,PLPRATETuningCoverageSimulation,2026-01-16 09:42,123.22425887982051,3.12.9,scripts/plm/plpr_ate_tune_config.yml +0.11.2.dev96,PLPRATETuningCoverageSimulation,2026-01-16 16:54,76.14726469516754,3.12.9,scripts/plm/plpr_ate_tune_config.yml diff --git a/scripts/plm/plpr_ate_config.yml b/scripts/plm/plpr_ate_config.yml index 7175b2f..66a3182 100644 --- a/scripts/plm/plpr_ate_config.yml +++ b/scripts/plm/plpr_ate_config.yml @@ -9,7 +9,7 @@ simulation_parameters: dgp_parameters: theta: [0.5] # Treatment effect dim_x: [10] # Number of covariates - num_id: [250] # number of units + num_id: [1000] # number of units num_t: [10] # number of time periods DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes diff --git a/scripts/plm/plpr_ate_tune_config.yml b/scripts/plm/plpr_ate_tune_config.yml index 8544cb2..281ead8 100644 --- a/scripts/plm/plpr_ate_tune_config.yml +++ b/scripts/plm/plpr_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 200 + repetitions: 100 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 @@ -9,9 +9,9 @@ simulation_parameters: dgp_parameters: theta: [0.5] # Treatment effect dim_x: [10] # Number of covariates - num_id: [250] # number of units + num_id: [1000] # number of units num_t: [10] # number of time periods - DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes + DGP: ["dgp1", "dgp3"] # Data generating processes # Define reusable learner configurations learner_definitions: