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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 |
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 |
Reference manual: | GenericML.pdf |
Package source: | GenericML_0.2.2.tar.gz |
Windows binaries: | r-devel: GenericML_0.2.2.zip, r-release: GenericML_0.2.2.zip, r-oldrel: GenericML_0.2.2.zip |
macOS binaries: | r-release (arm64): GenericML_0.2.2.tgz, r-oldrel (arm64): GenericML_0.2.2.tgz, r-release (x86_64): GenericML_0.2.2.tgz, r-oldrel (x86_64): GenericML_0.2.2.tgz |
Old sources: | GenericML archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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