20: Modified Causal Forest for Heterogeneous Treatment Effects with Structurally Constraining Covariates

Thomas M. Menino Convention & Exhibition Center 

To improve generalizability of clinical trial results, studies may allow participation from individuals ineligible for one study treatment, as in the Biomarkers for Evaluating Spine Treatment (BEST) Trial. In this setting, challenges arise when estimating heterogenous treatment effects (HTEs) for a candidate biomarker that also influences treatment eligibility. This biomarker may simultaneously act as a prognostic factor, treatment effect modifier, and impact treatment assignment. Standard HTE approaches fail to account for these joint mechanisms, leading to biased estimates for the target population. We propose a framework that integrates inverse probability weighting with causal forests to address threats to generalizability arising from structural randomization constraints. This approach performs a dual adjustment for differences across eligibility subgroups and restricted treatment randomization, ensuring that estimated conditional average treatment effects (CATEs) are representative of the target population. We discuss the estimator's theoretical properties, show improved performance through simulations, and compare it to the standard causal forest approach using data from the BEST Trial.

Precision Health

Heterogeneous Treatment Effects

Causal Forest

Clinical Trial

Generalizability

Causal inference 

Biometrics Section