A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect (2310.06746v2)

Abstract: Interpretability plays a critical role in the application of statistical learning for estimating heterogeneous treatment effects (HTE) for complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL) to estimate and enhance our understanding of HTE for atrial septal defect, addressing an overlooked question in previous literature: what if an individual simultaneously belongs to multiple groups with different average treatment effects? The CRL process consists of three steps: rule discovery, which generates a set of causal rules with corresponding subgroup average treatment effects; rule selection, which identifies a subset of these rules to deconstruct individual-level treatment effects as a linear combination of subgroup-level effects; and rule analysis, which outlines a detailed procedure for further analyzing each selected rule from multiple perspectives to identify the most promising rules for validation. Extensive simulation studies and real-world data analysis demonstrate that CRL outperforms other methods in providing interpretable estimates of HTE, especially when dealing with complex ground truth and sufficient sample sizes.