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policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
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>.
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Package details |
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| Author | Erik Sverdrup [aut, cre], Ayush Kanodia [aut], Zhengyuan Zhou [aut], Susan Athey [aut], Stefan Wager [aut] |
| Maintainer | Erik Sverdrup <erik.sverdrup@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.2.5 |
| URL | https://github.com/grf-labs/policytree |
| Package repository | View on CRAN |
| Installation |
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