A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
This work addresses interpretable HTE estimation for complex diseases like atrial septal defect, offering a domain-specific incremental improvement by handling overlapping subgroups.
The authors tackled the problem of estimating heterogeneous treatment effects (HTE) in complex diseases by proposing a causal rule learning (CRL) approach, which outperformed other methods in providing interpretable HTE estimates, especially with complex ground truth and sufficient sample sizes.
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? 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 presents 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.