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Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (n.d.) <arXiv:2004.14497>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.
Package details |
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Author | Drew Dimmery [aut, cre, cph] (<https://orcid.org/0000-0001-9602-6325>) |
Maintainer | Drew Dimmery <drew.dimmery@univie.ac.at> |
License | MIT + file LICENSE |
Version | 1.0.2 |
URL | https://github.com/ddimmery/tidyhte https://ddimmery.github.io/tidyhte/index.html |
Package repository | View on CRAN |
Installation |
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