Philippe Boileau, Ning Leng, Nima Hejazi, Mark van der Laan, and Sandrine Dudoit
Heterogeneous treatment effects are driven by treatment effect modifiers (TEMs), pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering TEMs are limited to low-dimensional data, data with weakly correlated covariates, or parametric data-generating processes. We resolve these issues by developing a framework for defining model-agnostic TEM variable importance parameters appropriate for high-dimensional data with arbitrary correlation structure, deriving causal machine learning estimators of these parameters, and establishing these estimators' asymptotic properties. Simulation experiments demonstrate that these estimators' asymptotic guarantees are approximately achieved in realistic sample sizes for observational and randomized studies alike. This framework is applied to gene expression data collected during a clinical trial investigating the effect of a novel treatment on disease-free survival in breast cancer.
- A flexible approach for predictive biomarker discovery by Boileau et al. (2022)
- A nonparametric framework for treatment effect modifier discovery in high dimensions by Boileau et al. (2025)