论文标题
最佳政策树
Optimal Policy Trees
论文作者
论文摘要
我们提出了一种直接从数据中学习基于树的处方政策的方法，结合了从因果推理文献中进行反事实估算的方法，以及在全球最佳决策树方面的最新进展。所得的方法，最佳的政策树产生了可解释的处方政策，具有高度可扩展的，并且可以处理离散和连续处理。我们对合成数据集和现实数据集进行了广泛的实验，并证明这些树木在各种问题上都提供了一流的性能。
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.
