Primary Submission Category: Heterogeneous Treatment Effects
A nonparametric framework for treatment effect modifier discovery in high dimensions
Authors: Philippe Boileau, Ning Leng, Nima Hejazi, Mark van der Laan, Sandrine Dudoit,
Presenting Author: Philippe Boileau*
Current approaches for uncovering treatment effect modifiers are limited to low-dimensional data or data with weakly correlated confounders, or are restricted to simple data-generating processes. We develop a general framework for defining model-agnostic treatment effect modifier variable importance parameters applicable to high-dimensional data with arbitrary correlation structure, deriving nonparametric estimators of these parameters, and establishing these estimators’ asymptotic properties. We showcase this framework by deriving risk-difference- and relative-risk-based treatment effect modifier variable importance parameters for data-generating processes with continuous, binary and time-to-event outcomes with binary exposures and potentially high-dimensional confounders. One-step, estimating equation and targeted maximum likelihood estimators for each parameter are provided. Certain estimators are proven to be double-robust under non-stringent conditions. All are asymptotically linear under reasonable entropy constraints on the data-generating process and consistency-rate requirements on the nuisance parameter estimators. Numerical experiments with moderate- and high-dimensional confounders demonstrate that these estimators’ asymptotic
guarantees, like false discovery rate control, are approximately achieved in realistic sample sizes for observational and randomized studies alike.