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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "policytree" in publications use:'
type: software
license: MIT
title: 'policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization
  over Trees'
version: 1.2.5
doi: 10.32614/CRAN.package.policytree
abstract: 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) <https://doi.org/10.21105/joss.02232>, Athey and Wager (2021) <https://doi.org/10.3982/ECTA15732>,
  and Zhou, Athey, and Wager (2023) <https://doi.org/10.1287/opre.2022.2271>.
authors:
- family-names: Sverdrup
  given-names: Erik
  email: erik.sverdrup@gmail.com
- family-names: Kanodia
  given-names: Ayush
- family-names: Zhou
  given-names: Zhengyuan
- family-names: Athey
  given-names: Susan
- family-names: Wager
  given-names: Stefan
repository: https://erikcs.r-universe.dev
repository-code: https://github.com/grf-labs/policytree
commit: 9919d4b13e1e344362f7f22cdb7ae7c37c0f7727
url: https://github.com/grf-labs/policytree
date-released: '2026-08-04'
contact:
- family-names: Sverdrup
  given-names: Erik
  email: erik.sverdrup@gmail.com