A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
用于可解释异质性处理效应估计的因果规则学习新方法
Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL), to estimate and improve our understanding of HTE for atrial septal defect, addressing an overlooked question in the previous literature: what if an individual simultaneously belongs to multiple groups with different average treatment effects?…