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.

Version: 0.1.1
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
Suggests: knitr, rmarkdown, testthat, patrick, mlr3learners (≥ 0.3.0), paradox (≥ 0.4.0), dplyr, glmnet, lgr, ranger, sandwich, AER, rpart
Published: 2020-12-16
Author: Philipp Bach [aut], Victor Chernozhukov [aut], Malte S. Kurz [aut, cre], Martin Spindler [aut]
Maintainer: Malte S. Kurz <malte.simon.kurz at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: DoubleML results


Reference manual: DoubleML.pdf
Vignettes: Getting Started with DoubleML
Installing DoubleML
Package source: DoubleML_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: DoubleML_0.1.1.tgz, r-oldrel: DoubleML_0.1.1.tgz


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