DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

TL;DR

The paper introduces the DoubleML R package, implementing the double machine learning framework for causal inference, emphasizing its object-oriented design for flexibility and extension, demonstrated through simulated and real data examples.

Contribution

It provides an object-oriented R implementation of the double machine learning framework, enabling flexible and extendable causal inference with machine learning methods.

Findings

Effective inference in causal models using DoubleML

Compatibility with various machine learning methods

Demonstrated with simulated and real data examples

Abstract

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.