Package: policytree
Title: Policy Learning via Doubly Robust Empirical Welfare Maximization
        over Trees
Version: 1.2.1
Authors@R: c(
    person("Erik", "Sverdrup", role = c("aut", "cre"), email = "erikcs@stanford.edu"),
    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 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.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
Suggests: testthat (>= 3.0.4), DiagrammeR
RoxygenNote: 7.2.1
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: 2022-11-20 03:30:23 UTC; erikcs
Author: Erik Sverdrup [aut, cre],
  Ayush Kanodia [aut],
  Zhengyuan Zhou [aut],
  Susan Athey [aut],
  Stefan Wager [aut]
Maintainer: Erik Sverdrup <erikcs@stanford.edu>
Repository: CRAN
Date/Publication: 2022-11-20 04:10:02 UTC
