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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) <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) <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.
Getting started
Browse package contents
Package details |
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| Author | Max Welz [aut, cre] (<https://orcid.org/0000-0003-2945-1860>), Andreas Alfons [aut] (<https://orcid.org/0000-0002-2513-3788>), Mert Demirer [aut], Victor Chernozhukov [aut] |
| Maintainer | Max Welz <welz@ese.eur.nl> |
| License | GPL (>= 3) |
| Version | 0.2.2 |
| URL | https://github.com/mwelz/GenericML/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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