Related papers: Optimal Policy Trees

Optimal Policy Trees

URL: http://arxiv.org/abs/2012.02279v1
Date: Thu, 3 Dec 2020 21:41:22 GMT
Title: Optimal Policy Trees
Authors: Maxime Amram, Jack Dunn, Ying Daisy Zhuo
Abstract summary: We propose an approach for learning optimal tree-based prescription policies directly from data. We combine methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees. The resulting method, Optimal Policy Trees, yields interpretable prescription policies.
Score: 0.6015898117103068
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems.

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