Optimal policy trees

被引：0

作者：

Maxime Amram

Jack Dunn

Ying Daisy Zhuo

机构：

[1] Interpretable AI,

来源：

Machine Learning | 2022年 / 111卷

关键词：

Machine learning; Decision trees; Prescriptive decision making;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：2741 / 2768

页数：27