标题： 局部变量重要性度量在异质处理效应上的推断 显示英文标题

标题： Inference on Local Variable Importance Measures for Heterogeneous Treatment Effects

摘要： 我们提供了一个推断框架，用于评估异质处理效应的变量重要性。 这种评估在高风险领域（如医学）中尤其有用，因为在这些领域中，决策者不愿意依赖黑箱治疗推荐算法。 我们考虑的变量重要性度量是局部的，这意味着它们可能因个体而异，而推断是全局的，即检验给定变量是否对任何个体都是重要的。 我们的方法建立在函数值参数半参数理论的最新发展之上，并且即使在使用统计机器学习算法来量化处理效应异质性时也有效。 我们展示了该方法在传染病预防策略中的适用性。 显示英文摘要

摘要： We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, where decision makers hesitate to rely on black-box treatment recommendation algorithms. The variable importance measures we consider are local in that they may differ across individuals, while the inference is global in that it tests whether a given variable is important for any individual. Our approach builds on recent developments in semiparametric theory for function-valued parameters, and is valid even when statistical machine learning algorithms are employed to quantify treatment effect heterogeneity. We demonstrate the applicability of our method to infectious disease prevention strategies.