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) . This package's workhorse is the 'mlr3' framework of Lang et al. (2019) , 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) 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.


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install.packages("GenericML")

0.1.0 by Max Welz, 3 days ago


https://github.com/mwelz/GenericML/


Report a bug at https://github.com/mwelz/GenericML/issues/


Browse source code at https://github.com/cran/GenericML


Authors: Max Welz [aut, cre] , Andreas Alfons [aut] , Mert Demirer [aut] , Victor Chernozhukov [aut]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports sandwich, lmtest, splitstackshape, stats, parallel

Depends on ggplot2, mlr3, mlr3learners

Suggests glmnet, ranger, rpart, e1071, testthat


See at CRAN