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DoubleML

doubleml-for-py

DoubleML - Double Machine Learning in Python

Model DevelopmentClassical MLPython
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创建于 2020-09-09 · 更新于 2026-10-15 · 今日第 13744 名
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SvenKlaassenMalteKurzJanTeichertKluge

DoubleML/doubleml-for-py 排名第 13,744，有 794 个星标。

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DoubleML/doubleml-for-py 排名第 13,744，此处有 8 个同类条目；zhaogaofeng611/TextMatch 排名第 13,742。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Classical ML
星标数794
分叉数126
贡献者15
创建日期2020-09-09
更新日期2026-10-15
1 天星标数0
7 天星标数2
排名13,744
上次排名6,403
排名变化-7,341
7 天星标排名8,489
上次 7 天星标排名8,564
7 天星标排名变化75
总星标排名13,431
上次总星标排名13,407
总星标排名变化-24
README 摘录

DoubleML - Double Machine Learning in Python The Python package **DoubleML** provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. It is built on top of scikit-learn (Pedregosa et al. Note that the Python package was developed together with an R twin based on mlr3. The R package is also available on GitHub and Documentation and Maintenance Documentation and website: **DoubleML** is currently maintained by @PhilippBach and @SvenKlaassen. Bugs can be reported to the issue

tracker at Main Features Double / debiased machine learning (Chernozhukov et al. (2018)) for Partially linear regression models (PLR) Partially linear IV regression models (PLIV) Interactive regression models (IRM) Interactive IV regression models (IIVM) The object-oriented implementation of DoubleML is very flexible. The model classes , , and implement the estimation of the nuisance functions via machine learning methods and the computation of the Neyman orthogonal score function. All other functionalities are implemented in the abstract base class . In particular functionalities to

estimate double machine learning models and to perform statistical inference via the methods , , , and . This object-oriented implementation allows a high flexibility for the model specification in terms of . the machine learners for the nuisance functions, . the double machine learning algorithm, . the Neyman orthogonal score functions, . It further can be readily extended with regards to . new model classes that come with Neyman orthogonal score functions being linear in the target parameter, . alternative score functions via callables, . Installation **DoubleML** requires Python

scikit-learn numpy scipy pandas statsmodels joblib To install DoubleML with pip use DoubleML can be installed from source via For development we recommend uv, which sets up the environment (including the dependency group) in one step: Detailed installation instructions can be found in the documentation. Contributing DoubleML is a community effort. To get started for your first contribution we recommend reading our contributing guidelines and our code of conduct. Citation If you use the DoubleML package a citation is

highly appreciated: You can click the **"Cite this repository"** button at the top of the GitHub page to cite the package directly. Alternatively, you can cite the following paper: Bach, P. (2022), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python, Journal of Machine Learning Research, 23(53): 1-6, Bibtex-entry: Acknowledgements Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) is acknowledged – Project Number 431701914 and Grant GRK 2805/1. (2022), DoubleML - An Object-Oriented Implementation

of Double Machine Learning in Python, Journal of Machine Learning Research, 23(53): 1-6, Chernozhukov, V. (2018), Double/debiased machine learning for treatment and structural parameters. (2011), Scikit-learn: Machine Learning in Python.

常见问题

记录了多少星标和分叉？

DoubleML/doubleml-for-py 有 794 个星标和 126 个分叉。

仓库排名是多少？

DoubleML/doubleml-for-py 排名第 13,744。

记录的语言和类别是什么？

DoubleML/doubleml-for-py 有 794 个星标，语言为 Python，类别为 Model Development。

哪个仓库排名最接近？

DoubleML/doubleml-for-py 与 zhaogaofeng611/TextMatch 比较，后者排名第 13,742，有 795 个星标。

README

DoubleML - Double Machine Learning in Python

The Python package DoubleML provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018). It is built on top of scikit-learn (Pedregosa et al., 2011).

Note that the Python package was developed together with an R twin based on mlr3. The R package is also available on GitHub and .

Documentation and Maintenance

Documentation and website: https://docs.doubleml.org/

DoubleML is currently maintained by @PhilippBach and @SvenKlaassen.

Bugs can be reported to the issue tracker at https://github.com/DoubleML/doubleml-for-py/issues.

Main Features

Double / debiased machine learning (Chernozhukov et al. (2018)) for

  • Partially linear regression models (PLR)
  • Partially linear IV regression models (PLIV)
  • Interactive regression models (IRM)
  • Interactive IV regression models (IIVM)

The object-oriented implementation of DoubleML is very flexible. The model classes DoubleMLPLR, DoubleMLPLIV, DoubleMLIRM and DoubleIIVM implement the estimation of the nuisance functions via machine learning methods and the computation of the Neyman orthogonal score function. All other functionalities are implemented in the abstract base class DoubleML. In particular functionalities to estimate double machine learning models and to perform statistical inference via the methods fit, bootstrap, confint, p_adjust and tune. This object-oriented implementation allows a high flexibility for the model specification in terms of ...

  • ... the machine learners for the nuisance functions,
  • ... the resampling schemes,
  • ... the double machine learning algorithm,
  • ... the Neyman orthogonal score functions,
  • ...

It further can be readily extended with regards to

  • ... new model classes that come with Neyman orthogonal score functions being linear in the target parameter,
  • ... alternative score functions via callables,
  • ... alternative resampling schemes,
  • ...

Installation

DoubleML requires

  • Python
  • scikit-learn
  • numpy
  • scipy
  • pandas
  • statsmodels
  • joblib

To install DoubleML with pip use

pip install -U DoubleML

DoubleML can be installed from source via

git clone git@github.com:DoubleML/doubleml-for-py.git
cd doubleml-for-py
pip install --editable .

For development we recommend uv, which sets up the environment (including the dev dependency group) in one step:

git clone git@github.com:DoubleML/doubleml-for-py.git
cd doubleml-for-py
uv sync --extra rdd

Detailed installation instructions can be found in the documentation.

Contributing

DoubleML is a community effort. Everyone is welcome to contribute. To get started for your first contribution we recommend reading our contributing guidelines and our code of conduct.

Citation

If you use the DoubleML package a citation is highly appreciated:

You can click the "Cite this repository" button at the top of the GitHub page to cite the package directly. Alternatively, you can cite the following paper:

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.

Bibtex-entry:

@article{DoubleML2022,
      title   = {{DoubleML} -- {A}n Object-Oriented Implementation of Double Machine Learning in {P}ython},
      author  = {Philipp Bach and Victor Chernozhukov and Malte S. Kurz and Martin Spindler},
      journal = {Journal of Machine Learning Research},
      year    = {2022},
      volume  = {23},
      number  = {53},
      pages   = {1--6},
      url     = {http://jmlr.org/papers/v23/21-0862.html}
}

Acknowledgements

Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) is acknowledged – Project Number 431701914 and Grant GRK 2805/1.

References

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.

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68. doi:10.1111/ectj.12097.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011), Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12: 2825--2830, https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html.

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