Package: policytree
Title: Policy Learning via Doubly Robust Empirical Welfare Maximization
        over Trees
Version: 1.2.5
Authors@R: c(
    person("Erik", "Sverdrup", role = c("aut", "cre"), email = "erik.sverdrup@gmail.com"),
    person("Ayush", "Kanodia", role = "aut"),
    person("Zhengyuan", "Zhou", role = "aut"),
    person("Susan", "Athey", role = "aut"),
    person("Stefan", "Wager", role = "aut")
    )
Description: Learn optimal policies via doubly robust empirical welfare
        maximization over trees. Given reward estimates, the algorithm
        finds a rule-based treatment allocation, where the policy takes
        the form of a shallow decision tree that is globally optimal
        (or nearly so). Methods are described in Sverdrup, Kanodia,
        Zhou, Athey, and Wager (2020) <doi:10.21105/joss.02232>, Athey
        and Wager (2021) <doi:10.3982/ECTA15732>, and Zhou, Athey, and
        Wager (2023) <doi:10.1287/opre.2022.2271>.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: testthat (>= 3.0.4), DiagrammeR
RoxygenNote: 7.3.3
LinkingTo: Rcpp, BH
Imports: Rcpp, grf (>= 2.0.0)
URL: https://github.com/grf-labs/policytree
BugReports: https://github.com/grf-labs/policytree/issues
NeedsCompilation: yes
Packaged: 2026-10-04 09:48:49 UTC; root
Author: Erik Sverdrup [aut, cre], Ayush Kanodia [aut], Zhengyuan Zhou
        [aut], Susan Athey [aut], Stefan Wager [aut]
Maintainer: Erik Sverdrup <erik.sverdrup@gmail.com>
Config/pak/sysreqs: make
Repository: https://erikcs.r-universe.dev
Date/Publication: 2026-08-04 10:30:29 UTC
RemoteUrl: https://github.com/cran/policytree
RemoteRef: HEAD
RemoteSha: 9919d4b13e1e344362f7f22cdb7ae7c37c0f7727