DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
This work provides a practical tool for researchers and practitioners in causal inference, but it is incremental as it implements an existing framework.
The authors tackled the implementation of the double machine learning framework for causal inference by developing DoubleML, an open-source Python library that provides functionalities for valid statistical inference on causal parameters using machine learning methods, resulting in a flexible and extendable object-oriented tool.
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.