_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-policytree 1.2.5
Propagated dependencies: r-rcpp@1.1.1-1.1 r-grf@2.6.1 r-bh@1.90.0-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/grf-labs/policytree
Licenses: Expat
Build system: r
Synopsis: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
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>.

Total packages: 1