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tidyhte: Tidy Estimation of Heterogeneous Treatment Effects
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.
Getting started
Browse package contents
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 |
Install the latest version of this package by entering the following in R:
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