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: 1.0.2 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: 2025-04-11 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 uni-hamburg.de> BugReports: https://github.com/DoubleML/doubleml-for-r/issues License: MIT + file LICENSE URL: https://docs.doubleml.org/stable/index.html, https://github.com/DoubleML/doubleml-for-r/ NeedsCompilation: no Citation: DoubleML citation info Materials: README In views: CausalInference, Econometrics, MachineLearning CRAN checks: DoubleML results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=DoubleML to link to this page.
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