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.

翻译：在应用统计学习估计复杂疾病中的异质性处理效应时，可解释性起着至关重要的作用。本研究采用基于规则的工作流程，即因果规则学习，来估计并增进对房间隔缺损异质性处理效应的理解，解决了先前文献中一个被忽视的问题：如果个体同时属于具有不同平均处理效应的多个群体，情况会如何？CRL流程包含三个步骤：规则发现，生成一组带有相应亚组平均处理效应的因果规则；规则选择，识别这些规则的一个子集，将个体层面的处理效应解构为亚组层面效应的线性组合；以及规则分析，提供了一个详细程序，用于从多个角度进一步分析每条选定规则，以识别最有希望进行验证的规则。大量的模拟研究和真实世界数据分析表明，CRL在提供可解释的异质性处理效应估计方面优于其他方法，尤其是在处理复杂真实情况和充足样本量时。