Targeting relative risk heterogeneity with causal forests

The estimation of heterogeneous treatment effects (HTE) across different subgroups in a population is of significant interest in clinical trial analysis. State-of-the-art HTE estimation methods, including causal forests (Wager and Athey, 2018), generally rely on recursive partitioning for non-parametric identification of relevant covariates and interactions. However, like many other methods in this area, causal forests partition subgroups based on differences in absolute risk. This can dilute statistical power by masking variability 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 exhaustive generalized linear model comparison. We present results that suggest relative risk causal forests can capture otherwise undetected sources of heterogeneity.