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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0.1.0 by Max Welz, 3 days ago

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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