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

2023-10-10

AI Summary

This work addresses the interpretability bottleneck in heterogeneous treatment effect (HTE) estimation for complex diseases—e.g., atrial septal defect—particularly when individuals simultaneously belong to multiple, opposing effect subgroups, complicating attribution. We propose the first Causal Rule Learning (CRL) framework, which decomposes individual-level HTE into interpretable, linear contributions via a three-stage pipeline: causal rule discovery, selection, and analysis—thereby relaxing the conventional mutual-exclusivity assumption on subgroups. CRL integrates causal inference, interpretable rule mining, and multi-perspective evaluation. It is rigorously validated on both synthetic and real-world clinical datasets. Results demonstrate that, under conditions of mechanistic complexity and sufficient sample size, CRL significantly improves both the interpretability and accuracy of HTE estimation, outperforming state-of-the-art methods.

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.

Problem

• Estimates heterogeneous treatment effects (HTE)
• Addresses multiple group membership impacts
• Enhances interpretability using causal rule learning

Innovation

• Causal rule learning for HTE
• Three-step rule-based workflow
• Interpretable subgroup treatment effects