Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal.
| Version: | 1.2.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp, grf (≥ 2.0.0) |
| LinkingTo: | Rcpp, BH |
| Suggests: | testthat (≥ 3.0.4), DiagrammeR |
| Published: | 2024-06-13 |
| Author: | Erik Sverdrup [aut, cre], Ayush Kanodia [aut], Zhengyuan Zhou [aut], Susan Athey [aut], Stefan Wager [aut] |
| Maintainer: | Erik Sverdrup <erik.sverdrup at monash.edu> |
| BugReports: | https://github.com/grf-labs/policytree/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/grf-labs/policytree |
| NeedsCompilation: | yes |
| CRAN checks: | policytree results |
| Reference manual: | policytree.pdf |
| Package source: | policytree_1.2.3.tar.gz |
| Windows binaries: | r-devel: policytree_1.2.2.zip, r-release: policytree_1.2.3.zip, r-oldrel: policytree_1.2.3.zip |
| macOS binaries: | r-release (arm64): policytree_1.2.3.tgz, r-oldrel (arm64): policytree_1.2.3.tgz, r-release (x86_64): policytree_1.2.3.tgz, r-oldrel (x86_64): policytree_1.2.3.tgz |
| Old sources: | policytree archive |
| Reverse imports: | EpiForsk, polle |
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