Treatment effect heterogeneity (TEH), or variability in treatment effect for different subgroups within a population, is of significant interest in clinical trial analysis. Causal forests (Wager and Athey, 2018) is a highly popular method for this problem, but like many other methods for detecting TEH, its criterion for separating subgroups focuses on differences in absolute risk. This can dilute statistical power by masking nuance in the relative risk, which is often a more appropriate quantity of clinical interest. In this work, we propose and implement a methodology for modifying causal forests to target relative risk using a novel node-splitting procedure based on generalized linear model (GLM) comparison. We present results on simulated and real-world data that suggest relative risk causal forests can capture otherwise unobserved sources of heterogeneity.

翻译：治疗效应异质性（TEH），即人群中不同亚组治疗效果的变异性，在临床试验分析中具有重要研究价值。因果森林（Wager and Athey, 2018）是处理该问题的热门方法，但与其他检测TEH的方法类似，其亚组分隔标准侧重于绝对风险的差异。这可能会因掩盖相对风险（通常更符合临床关注的实际需求）的细微差异而削弱统计功效。本研究提出并实现了一种改进因果森林的方法，通过基于广义线性模型（GLM）比较的新型节点分裂程序，将其目标导向相对风险。我们在模拟数据和真实数据上的结果表明，相对风险因果森林能够捕捉到其他方法无法观察到的异质性来源。