DoubleML: Double Machine Learning in R

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Version: 1.0.1
Depends: R (≥ 3.5.0)
Imports: R6 (≥ 2.4.1), data.table (≥ 1.12.8), stats, checkmate, mlr3 (≥ 0.5.0), mlr3tuning (≥ 0.3.0), mvtnorm, utils, clusterGeneration, readstata13, mlr3learners (≥ 0.3.0), mlr3misc
Suggests: knitr, rmarkdown, testthat, covr, patrick (≥ 0.1.0), paradox (≥ 0.4.0), dplyr, glmnet, lgr, ranger, sandwich, AER, rpart, bbotk, mlr3pipelines
Published: 2024-06-05
DOI: 10.32614/CRAN.package.DoubleML
Author: Philipp Bach [aut, cre], Victor Chernozhukov [aut], Malte S. Kurz [aut], Martin Spindler [aut], Klaassen Sven [aut]
Maintainer: Philipp Bach <philipp.bach at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: DoubleML citation info
Materials: README
In views: CausalInference, Econometrics, MachineLearning
CRAN checks: DoubleML results


Reference manual: DoubleML.pdf
Vignettes: DoubleML - An Object-Oriented Implementation of Double Machine Learning in R
Getting Started with DoubleML
Installing DoubleML


Package source: DoubleML_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): DoubleML_1.0.1.tgz, r-oldrel (arm64): DoubleML_1.0.1.tgz, r-release (x86_64): DoubleML_1.0.1.tgz, r-oldrel (x86_64): DoubleML_1.0.1.tgz
Old sources: DoubleML archive

Reverse dependencies:

Reverse suggests: drape, gKRLS


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