Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <doi:10.48550/arXiv.1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <doi:10.48550/arXiv.1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.
Version: 0.2.2 Depends: ggplot2, mlr3, mlr3learners Imports: sandwich, lmtest, splitstackshape, stats, parallel, abind Suggests: glmnet, ranger, rpart, e1071, xgboost, kknn, DiceKriging, testthat (≥ 3.0.0) Published: 2022-06-18 DOI: 10.32614/CRAN.package.GenericML Author: Max Welz [aut, cre], Andreas Alfons [aut], Mert Demirer [aut], Victor Chernozhukov [aut] Maintainer: Max Welz <welz at ese.eur.nl> BugReports: https://github.com/mwelz/GenericML/issues/ License: GPL (≥ 3) URL: https://github.com/mwelz/GenericML/ NeedsCompilation: no Citation: GenericML citation info Materials: NEWS CRAN checks: GenericML results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=GenericML to link to this page.
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