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DoubleML - Double Machine Learning in Python
| Date | Stars |
|---|---|
| 2026-07-31 | 765 |
| 2026-08-06 | 765 |
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# DoubleML - Double Machine Learning in Python <a href="https://docs.doubleml.org"><img src="https://raw.githubusercontent.com/DoubleML/doubleml-for-py/main/doc/logo.png" align="right" width = "120" /></a> [](https://github.com/DoubleML/doubleml-for-py/actions?query=workflow%3Abuild) [](https://badge.fury.io/py/DoubleML) [](https://anaconda.org/conda-forge/doubleml) [](https://codecov.io/gh/DoubleML/doubleml-for-py) [](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) [](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 [](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 [@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, - ...  ## 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
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matched fp:95ad7fa42ddcd2ae, llm:topics: causal-inference, double-machine-learning, econometrics, statistics; description: 'DoubleML - Double Machine Learning in Python'
matched fp:95ad7fa42ddcd2ae, llm:topics: causal-inference, double-machine-learning, econometrics, statistics; description: 'DoubleML - Double Machine Learning in Python'