Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond

TL;DR

This paper introduces a statistical framework for assessing variable importance in treatment effect heterogeneity, enabling reliable inference even with complex machine learning models, crucial for high-stakes fields like medicine.

Contribution

It develops a novel inferential method based on semiparametric theory to evaluate variable importance across individuals, applicable with modern machine learning techniques.

Findings

Method validated on infectious disease prevention data.

Framework allows for individual-specific importance assessment.

Inference remains valid with complex machine learning models.

Abstract

We provide an inferential framework to assess variable importance for heterogeneous treatment effects. This assessment is especially useful in high-risk domains such as medicine, where decision makers hesitate to rely on black-box treatment recommendation algorithms. The variable importance measures we consider are local in that they may differ across individuals, while the inference is global in that it tests whether a given variable is important for any individual. Our approach builds on recent developments in semiparametric theory for function-valued parameters, and is valid even when statistical machine learning algorithms are employed to quantify treatment effect heterogeneity. We demonstrate the applicability of our method to infectious disease prevention strategies.