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

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

machine learning, causal inference, causal machine learning, R, mlr3, object orientation.

The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consists 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.

Philipp Bach

Philipp Bach

University of Hamburg

March 2024

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