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:
Estimation of nuisance components can be performed by various
state-of-the-art machine learning methods that are available in the
mlr3 ecosystem (Lang et al., 2019). 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.
A long version of this package vignette is available in the accompanying publication in the Journal of Statistical Software at doi:10.18637/jss.v108.i03
Bach, P., Chernozhukov, V., Kurz, M. S., Spindler, M. and Klaassen, S. (2024), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, Journal of Statistical Software, 108(3): 1-56, doi:10.18637/jss.v108.i03, arXiv:2103.09603.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21: C1-C68, URL: https://doi.org/10.1111/ectj.12097.
Lang, M., Binder, M., Richter, J., Schratz, P., Pfisterer, F., Coors, S., Au, Q., Casalicchio, G., Kotthoff, L. and Bischl, B. (2019), mlr3: A modern object-oriented machine learing framework in R. Journal of Open Source Software, https://doi.org/10.21105/joss.01903, URL: https://mlr3.mlr-org.com/.