  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.
