policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

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
DOI: 10.32614/CRAN.package.policytree
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_1.2.3.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 dependencies:

Reverse imports: EpiForsk, polle


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