  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.
