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

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: 1.1.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, grf (≥ 2.0.0)
LinkingTo: Rcpp, BH
Suggests: testthat (≥ 2.1.0), DiagrammeR
Published: 2021-06-24
Author: Erik Sverdrup [aut, cre], Ayush Kanodia [aut], Zhengyuan Zhou [aut], Susan Athey [aut], Stefan Wager [aut]
Maintainer: Erik Sverdrup <erikcs at stanford.edu>
BugReports: https://github.com/grf-labs/policytree/issues
License: GPL-3
URL: https://github.com/grf-labs/policytree
NeedsCompilation: yes
CRAN checks: policytree results


Reference manual: policytree.pdf
Package source: policytree_1.1.0.tar.gz
Windows binaries: r-devel: policytree_1.1.0.zip, r-devel-UCRT: policytree_1.1.0.zip, r-release: policytree_1.1.0.zip, r-oldrel: policytree_1.1.0.zip
macOS binaries: r-release (arm64): policytree_1.1.0.tgz, r-release (x86_64): policytree_1.1.0.tgz, r-oldrel: policytree_1.1.0.tgz
Old sources: policytree archive


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