The R package DoubleML provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018). It is built on top of mlr3 and the mlr3 ecosystem (Lang et al., 2019).
Note that the R package was developed together with a python twin based on scikit-learn. The python package is also available on GitHub and .
Documentation of functions in R: https://docs.doubleml.org/r/stable/reference/index.html
User guide: https://docs.doubleml.org
DoubleML is currently maintained by @MalteKurz
and @PhilippBach
.
Double / debiased machine learning framework of Chernozhukov et al. (2018) for
The object-oriented implementation of DoubleML that
is based on the R6 package for R is
very flexible. The model classes DoubleMLPLR
,
DoubleMLPLIV
, DoubleMLIRM
and
DoubleIIVM
implement the estimation of the nuisance
functions via machine learning methods and the computation of the Neyman
orthogonal score function. All other functionalities are implemented in
the abstract base class DoubleML
. In particular
functionalities to estimate double machine learning models and to
perform statistical inference via the methods fit
,
bootstrap
, confint
, p_adjust
and
tune
. This object-oriented implementation allows a high
flexibility for the model specification in terms of …
It further can be readily extended with regards to
Install the latest release from CRAN:
::packages("DoubleML") remotes
Install the development version from GitHub:
::install_github("DoubleML/doubleml-for-r") remotes
DoubleML requires
DoubleML is a community effort. Everyone is welcome to contribute. To get started for your first contribution we recommend reading our contributing guidelines and our code of conduct.
If you use the DoubleML package a citation is highly appreciated:
Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2021), DoubleML - An Object-Oriented Implementation of Double Machine Learning in R, arXiv:2103.09603.
Bibtex-entry:
@misc{DoubleML2020,
title={{DoubleML} -- {A}n Object-Oriented Implementation of Double Machine Learning in {R}},
author={P. Bach and V. Chernozhukov and M. S. Kurz and M. Spindler},
year={2021},
eprint={2103.09603},
archivePrefix={arXiv},
primaryClass={stat.ML},
note={arXiv:\href{https://arxiv.org/abs/2103.09603}{2103.09603} [stat.ML]}
}
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, 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., 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.