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  "Title": "Policy Learning via Doubly Robust Empirical Welfare Maximization\nover Trees",
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  "Description": "Learn optimal policies via doubly robust empirical welfare\nmaximization over trees. Given reward estimates, the algorithm\nfinds a rule-based treatment allocation, where the policy takes\nthe form of a shallow decision tree that is globally optimal\n(or nearly so). Methods are described in Sverdrup, Kanodia,\nZhou, Athey, and Wager (2020) <doi:10.21105/joss.02232>, Athey\nand Wager (2021) <doi:10.3982/ECTA15732>, and Zhou, Athey, and\nWager (2023) <doi:10.1287/opre.2022.2271>.",
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