DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
Abstract
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods. The object-oriented implementation of DoubleML provides a high flexibility in terms of model specifications and makes it easily extendable. The package is distributed under the MIT license and relies on core libraries from the scientific Python ecosystem: scikit-learn, numpy, pandas, scipy, statsmodels and joblib. Source code, documentation and an extensive user guide can be found at https://github.com/DoubleML/doubleml-for-py and https://docs.doubleml.org.
Cite
@article{arxiv.2104.03220,
title = {DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python},
author = {Philipp Bach and Victor Chernozhukov and Malte S. Kurz and Martin Spindler},
journal= {arXiv preprint arXiv:2104.03220},
year = {2022}
}
Comments
6 pages, 2 figures