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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 imports: | EpiForsk, polle |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.