Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference
异质性治疗效应蒸馏:因果推断中的稳定亚组估计
Recent methodological developments have introduced new black-box approaches to better estimate heterogeneous treatment effects; however, these methods fall short of providing interpretable characterizations of the underlying individuals who may be most at risk or benefit most from receiving the treatment, thereby limiting their practical utility. In this work, we introduce causal distillation trees (CDT) to estimate interpretable subgroups. CDT allows researchers to fit any…