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SvenKlaassen merged 9 commits into from
Jun 19, 2025
Merged

Add Citation & Maintenance #246

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3eff92b
upd software citation
PhilippBach Jun 13, 2025
1678941
upd
PhilippBach Jun 13, 2025
413294a
update gama citation
SvenKlaassen Jun 16, 2025
1abe84d
add reference to preprint
esmucler Jun 16, 2025
99c474a
clear outputs
esmucler Jun 16, 2025
ce81973
rerun
esmucler Jun 16, 2025
b8c56d4
clear outputs (will rerun while building docs)
SvenKlaassen Jun 12, 2025
c4a0ed7
Merge pull request #244 from DoubleML/p-software-citation
SvenKlaassen Jun 17, 2025
af855c5
Merge pull request #245 from esmucler/add_reference
SvenKlaassen Jun 17, 2025
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2 changes: 1 addition & 1 deletion doc/examples/py_double_ml_meets_flaml.ipynb
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"\n",
"This notebook will use [FLAML](https://github.com/microsoft/FLAML), but there are also many other AutoML frameworks. Particularly useful for DoubleML are packages that provide some way to export the model in `sklearn`-style.\n",
"\n",
"Examples are: [TPOT](https://epistasislab.github.io/tpot/), [autosklearn](https://automl.github.io/auto-sklearn/master/), [H20](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) or [Gama](https://openml-labs.github.io/gama/master/)."
"Examples are: [TPOT](https://epistasislab.github.io/tpot/), [autosklearn](https://automl.github.io/auto-sklearn/master/), [H20](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) or [Gama](https://github.com/amore-labs/gama)."
]
},
{
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34 changes: 11 additions & 23 deletions doc/examples/py_double_ml_robust_iv.ipynb
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},
{
"cell_type": "markdown",
"id": "6c070d4e",
"metadata": {},
"source": [
"In this example we will show how to use the DoubleML package to obtain confidence sets for the treatment effects that are robust to weak instruments. Weak instruments are those that have a relatively weak correlation with the treatment. It is well known that in this case, standard methods to construct confidence intervals have poor properties and can have coverage much lower than the nominal value. We will assume that the reader of this notebook is already familiar with DoubleML and how it can be used to fit instrumental variable models.\n",
Expand All @@ -29,7 +30,8 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "f9e7a46f",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -46,6 +48,7 @@
},
{
"cell_type": "markdown",
"id": "b6b976ce",
"metadata": {},
"source": [
"# Running a small simulation"
Expand All @@ -61,7 +64,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "82111204",
"metadata": {},
"outputs": [],
Expand All @@ -86,7 +89,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"id": "9a347c25",
"metadata": {},
"outputs": [],
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},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "600b8196",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -163,26 +166,10 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "86c83edc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" DML coverage Robust coverage DML median length \\\n",
"instrument_strength \n",
"0.003 0.15 0.91 0.489567 \n",
"1.000 0.93 0.92 0.572717 \n",
"\n",
" Robust median length \n",
"instrument_strength \n",
"0.003 inf \n",
"1.000 0.582754 \n"
]
}
],
"outputs": [],
"source": [
"results_df = pd.DataFrame(output_list)\n",
"summary_df = results_df.groupby(\"instrument_strength\").agg(\n",
Expand Down Expand Up @@ -211,6 +198,7 @@
"- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., and Hansen, C. (2018). Double/debiased machine learning for\n",
"treatment and structural parameters. The Econometrics Journal, 21(1):C1–C68.\n",
"- Ma, Y. (2023). Identification-robust inference for the late with high-dimensional covariates. arXiv preprint arXiv:2302.09756.\n",
"- Smucler, E., Lanni, L., Masip, D. (2025). A note on the properties of the confidence set for the local average treatment effect obtained by inverting the score test. arXiv preprint 2506.10449\n",
"- Stock, J. H. and Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5):1055–1096.\n",
"- Takatsu, K., Levis, A. W., Kennedy, E., Kelz, R., and Keele, L. (2023). Doubly robust machine learning for an instrumental\n",
"variable study of surgical care for cholecystitis. arXiv preprint arXiv:2307.06269."
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
"version": "3.13.1"
}
},
"nbformat": 4,
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23 changes: 22 additions & 1 deletion doc/index.rst
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Expand Up @@ -200,20 +200,38 @@ and `https://github.com/DoubleML/doubleml-for-r/issues <https://github.com/Doubl
Citation
--------

If you use the DoubleML package a citation is highly appreciated:
If you use the DoubleML package a citation is highly appreciated. To cite the Python package, please use the following reference:

Bach, P., Chernozhukov, V., Klaassen, S., Kurz, M. S., & Spindler, M. DoubleML - Double
Machine Learning in Python [Computer software]. `https://github.com/DoubleML/doubleml-for-py <https://github.com/DoubleML/doubleml-for-py>`_.

An accompanying paper to the DoubleML package for Python has been published in the Journal of Machine Learning Research. For citation, please use the following reference:

Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An
Object-Oriented Implementation of Double Machine Learning in Python,
Journal of Machine Learning Research, 23(53): 1-6,
`https://www.jmlr.org/papers/v23/21-0862.html <https://www.jmlr.org/papers/v23/21-0862.html>`_.


For the R package, please use the following reference:

Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024),
DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, Journal of Statistical Software,
108(3): 1-56, doi:`10.18637/jss.v108.i03 <https://doi.org/10.18637/jss.v108.i03>`_, arXiv:`2103.09603 <https://arxiv.org/abs/2103.09603>`_.

Bibtex-entries:

.. code-block:: TeX

@software{DoubleML,
title = {{DoubleML} -- Double Machine Learning in Python},
author = {Bach, Philipp and Chernozhukov, Victor and Klaassen, Sven and Kurz, Malte S. and Spindler, Martin},
year = {2024},
version = {latest},
url = {https://github.com/DoubleML/doubleml-for-py},
note = {BSD-3-Clause License. Documentation: \url{https://docs.doubleml.org/stable/index.html}}
}

.. code-block:: TeX

@article{DoubleML2022Python,
Expand Down Expand Up @@ -250,6 +268,9 @@ Foundation) is acknowledged – Project Number 431701914.
References
----------

Bach, P., Chernozhukov, V., Klaassen, S., Kurz, M. S., & Spindler, M. DoubleML - Double
Machine Learning in Python [Computer software]. `https://github.com/DoubleML/doubleml-for-py <https://github.com/DoubleML/doubleml-for-py>`_.

Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An
Object-Oriented Implementation of Double Machine Learning in Python,
Journal of Machine Learning Research, 23(53): 1-6,
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