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.

我们提出并实施了一种方法，通过基于广义线性模型比较的新颖节点分割过程，修改因果森林以针对相对风险目标。我们展示了对模拟和真实数据的结果，表明相对风险因果森林可以捕捉到未观察到的异质性来源。

用因果森林目标相对风险异质性

Targeting Relative Risk Heterogeneity with Causal Forests

We apply causal forests to a dataset derived from the National Study of
Learning Mindsets, and consider resulting practical and conceptual challenges.
In particular, we discuss how causal forests use estimated propensity scores to
be more robust to confounding, and how they handle data with clustered errors.

本研究使用因果森林（causal forests）对来源于《学习心态国家研究》的数据进行了分析，讨论了因果森林使用估计的倾向得分更加具有鲁棒性以及如何处理包含聚类误差的数据等实际和概念上的挑战。