GenericML

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.

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