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https://github.com/DoubleML/doubleml-for-py

DoubleML - Double Machine Learning in Python
https://github.com/DoubleML/doubleml-for-py

causal-inference data-science double-machine-learning econometrics machine-learning python scikit-learn statistics

Last synced: 3 months ago
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DoubleML - Double Machine Learning in Python

Host: GitHub
URL: https://github.com/DoubleML/doubleml-for-py
Owner: DoubleML
License: bsd-3-clause
Created: 2020-09-09T13:27:25.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2024-08-12T18:52:50.000Z (3 months ago)
Last Synced: 2024-08-13T15:59:01.914Z (3 months ago)
Topics: causal-inference, data-science, double-machine-learning, econometrics, machine-learning, python, scikit-learn, statistics
Language: Python
Homepage: https://docs.doubleml.org
Size: 3.77 MB
Stars: 457
Watchers: 14
Forks: 70
Open Issues: 17
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-list - DoubleML - Double Machine Learning in Python. (Causal Inference / Others)

README

        # DoubleML - Double Machine Learning in Python 

[![build](https://github.com/DoubleML/doubleml-for-py/workflows/build/badge.svg)](https://github.com/DoubleML/doubleml-for-py/actions?query=workflow%3Abuild)

[![PyPI version](https://badge.fury.io/py/DoubleML.svg)](https://badge.fury.io/py/DoubleML)

[![Conda Version](https://img.shields.io/conda/vn/conda-forge/doubleml.svg)](https://anaconda.org/conda-forge/doubleml)

[![codecov](https://codecov.io/gh/DoubleML/doubleml-for-py/branch/main/graph/badge.svg?token=0BjlFPgdGk)](https://codecov.io/gh/DoubleML/doubleml-for-py)

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/1c08ec7d782c451784293c996537de14)](https://www.codacy.com/gh/DoubleML/doubleml-for-py/dashboard?utm_source=github.com&utm_medium=referral&utm_content=DoubleML/doubleml-for-py&utm_campaign=Badge_Grade)

[![Python version](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)](https://www.python.org/)

The Python package **DoubleML** provides an implementation of the double / debiased machine learning framework of

[Chernozhukov et al. (2018)](https://doi.org/10.1111/ectj.12097).

It is built on top of [scikit-learn](https://scikit-learn.org) (Pedregosa et al., 2011).

Note that the Python package was developed together with an R twin based on [mlr3](https://mlr3.mlr-org.com/).

The R package is also available on [GitHub](https://github.com/DoubleML/doubleml-for-r) and 

[![CRAN Version](https://www.r-pkg.org/badges/version/DoubleML)](https://cran.r-project.org/package=DoubleML).

## Documentation and Maintenance

Documentation and website: [https://docs.doubleml.org/](https://docs.doubleml.org/)

**DoubleML** is currently maintained by

[@MalteKurz](https://github.com/MalteKurz), [@PhilippBach](https://github.com/PhilippBach) and [@SvenKlaassen](https://github.com/SvenKlaassen).

Bugs can be reported to the issue tracker at

[https://github.com/DoubleML/doubleml-for-py/issues](https://github.com/DoubleML/doubleml-for-py/issues).

## Main Features

Double / debiased machine learning [(Chernozhukov et al. (2018))](https://doi.org/10.1111/ectj.12097) 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,

- ... 

![An overview of the OOP structure of the DoubleML package is given in the graphic available at https://github.com/DoubleML/doubleml-for-py/blob/main/doc/oop.svg](https://raw.githubusercontent.com/DoubleML/doubleml-for-py/main/doc/oop.svg)

## Installation

**DoubleML** requires

- Python

- sklearn

- numpy

- scipy

- pandas

- statsmodels

- joblib

To install DoubleML with pip use

```

pip install -U DoubleML

```

DoubleML can be installed from source via

```

git clone [email protected]:DoubleML/doubleml-for-py.git

cd doubleml-for-py

pip install --editable .

```

Detailed [installation instructions](https://docs.doubleml.org/stable/intro/install.html) 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](https://github.com/DoubleML/doubleml-for-py/blob/main/CONTRIBUTING.md)

and our

[code of conduct](https://github.com/DoubleML/doubleml-for-py/blob/main/CODE_OF_CONDUCT.md).

## Citation

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

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).

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.

## 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](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](https://doi.org/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](https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html).