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) <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 |
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