Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond
处理效应异质性的变量重要性推断:Shapley 值及其拓展
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…