从因果森林中蒸馏可解释的因果树
Distilling interpretable causal trees from causal forests
AI总结：
本文提出 Distilled Causal Tree，从因果森林蒸馏单棵可解释因果树，在噪声或高维相关特征场景下多数模拟优于原因果森林，并保持双重稳健与渐近正态。
摘要：
用于估计处理效应异质性的机器学习方法，相比那些仅检验少数预先设定假设的现有方法，有望提供更大的灵活性。然而，这些方法可能存在的一个问题是，从复杂的机器学习模型中提取洞见可能颇具挑战。条件平均处理效应的高维分布或许能够给出准确的个体层面估计，但其潜在模式可能难以理解，也难以获知分析的含义是什么。本文提出了 Distilled Causal Tree（蒸馏因果树），这是一种从因果森林中蒸馏出单棵可解释因果树的方法。该方法与现有的提取单棵树的方法相比表现良好，尤其是在噪声数据或存在许多相关特征的高维数据中。在这些情形下，它甚至在大多数模拟中优于作为基础的因果森林。与因果森林的估计一样，该方法的估计具有双重稳健性和渐近正态性。
英文摘要：
Machine learning methods for estimating treatment effect heterogeneity promise greater flexibility than existing methods that test a few pre-specified hypotheses. However, one problem these methods can have is that it can be challenging to extract insights from complicated machine learning models. A high-dimensional distribution of conditional average treatment effects may give accurate, individual-level estimates, but it can be hard to understand the underlying patterns; hard to know what the implications of the analysis are. This paper proposes the Distilled Causal Tree, a method for distilling a single, interpretable causal tree from a causal forest. This compares well to existing methods of extracting a single tree, particularly in noisy data or high-dimensional data where there are many correlated features. Here it even outperforms the base causal forest in most simulations. Its estimates are doubly robust and asymptotically normal just as those of the causal forest are.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
