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

• URL: http://arxiv.org/abs/2310.06746v2
• Date: Sun, 23 Feb 2025 11:02:53 GMT
• Title: A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
• Authors: Ying Wu, Hanzhong Liu, Kai Ren, Shujie Ma, Xiangyu Chang,
• Abstract summary: Interpretability plays a critical role in the application of statistical learning for estimating heterogeneous treatment effects.<n>In this study, we leverage a rule-based workflow to estimate and enhance our understanding of HTE for atrial septal defect.
• Score: 11.087188408510663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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