Learn optimal policies via doubly robust empirical welfare maximization over trees. This package implements the multi-action doubly robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where we want to learn policies that belong to the class of depth k decision trees.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp, grf (≥ 1.1.0) |
| LinkingTo: | Rcpp, BH |
| Suggests: | testthat (≥ 2.1.0), DiagrammeR |
| Published: | 2020-07-13 |
| Author: | Zhengyuan Zhou [aut], Susan Athey [aut], Stefan Wager [aut], Ayush Kanodia [aut], Erik Sverdrup [cre] |
| Maintainer: | Erik Sverdrup <erikcs at stanford.edu> |
| License: | GPL-3 |
| URL: | https://github.com/grf-labs/policytree |
| NeedsCompilation: | yes |
| CRAN checks: | policytree results |
| Reference manual: | policytree.pdf |
| Package source: | policytree_1.0.1.tar.gz |
| Windows binaries: | r-devel: policytree_1.0.1.zip, r-release: policytree_1.0.1.zip, r-oldrel: policytree_1.0.1.zip |
| macOS binaries: | r-release: policytree_1.0.1.tgz, r-oldrel: policytree_1.0.1.tgz |
| Old sources: | policytree archive |
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