Primary Submission Category: Heterogeneous Treatment Effects
Inference on Variable Importance Measures for Heterogeneous Treatment Effects
Authors: Pawel Morzywolek, Alex Luedtke,
Presenting Author: Pawel Morzywolek*
Recent years have seen a growing interest in quantifying treatment effect heterogeneity, which is vital for supporting individualized decision-making. Though black-box machine learning approaches might optimally predict treatment effect heterogeneity, in high-risk domains such as medicine, decision makers often hesitate to rely on decision support systems without understanding the underlying rationale behind the recommendations. Hence, it is crucial to offer insights into which variables best predict individualized treatment effects. Motivated by these considerations, we present model-agnostic variable importance measures for heterogeneous treatment effects. We provide efficient estimators of these measures together with corresponding confidence intervals, and introduce a Wald-type test to assess the null hypothesis of no importance. Our approach builds on recent developments in semiparametric theory for pathwise differentiable function-valued parameters, and is valid even when flexible black-box algorithms are employed to quantify treatment effect heterogeneity. We demonstrate the applicability of our methodology in the context of infectious disease prevention strategies.