GenericML: Generic Machine Learning Inference

Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <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) <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: 0.2.1
Depends: ggplot2, mlr3, mlr3learners
Imports: sandwich, lmtest, splitstackshape, stats, parallel, abind
Suggests: glmnet, ranger, rpart, e1071, testthat (≥ 3.0.0)
Published: 2022-05-12
Author: Max Welz ORCID iD [aut, cre], Andreas Alfons ORCID iD [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:

Reference manual: GenericML.pdf

Downloads:

Package source: GenericML_0.2.1.tar.gz
Windows binaries: r-devel: GenericML_0.2.1.zip, r-release: GenericML_0.2.1.zip, r-oldrel: GenericML_0.2.1.zip
macOS binaries: r-release (arm64): GenericML_0.2.1.tgz, r-oldrel (arm64): GenericML_0.2.1.tgz, r-release (x86_64): GenericML_0.2.1.tgz, r-oldrel (x86_64): GenericML_0.2.1.tgz
Old sources: GenericML archive

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