A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect

TL;DR

This paper introduces a causal rule learning method that enhances interpretability and accuracy in estimating heterogeneous treatment effects, especially for complex diseases, by identifying and analyzing multiple overlapping subgroup effects.

Contribution

The study proposes a novel causal rule learning framework that captures individuals belonging to multiple effect groups, improving interpretability and estimation of heterogeneous treatment effects.

Findings

CRL outperforms existing methods in simulations and real data.

It effectively identifies overlapping subgroup effects.

Provides detailed analysis for rule validation.

Abstract

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.