Variable importance measures for heterogeneous treatment effects

TL;DR

This paper introduces nonparametric variable importance measures for heterogeneous treatment effects, enabling better understanding of key drivers in complex CATE models using machine learning.

Contribution

It develops TE-VIMs based on MSE that quantify variable importance in CATE estimation, compatible with various ML strategies and estimators.

Findings

TE-VIMs effectively identify important variables in simulations.

Efficient estimators can be integrated with popular CATE meta-learners.

Application to clinical trial data demonstrates practical utility.

Abstract

Motivated by applications in precision medicine and treatment effect heterogeneity, recent research has focused on estimating conditional average treatment effects (CATEs) using machine learning (ML). CATE estimates may represent complicated functions that provide little insight into the key drivers of heterogeneity. Therefore, we introduce nonparametric treatment effect variable importance measures (TE-VIMs), based on the mean-squared error (MSE) in predicting the individual treatment effect. More precisely, TE-VIMs represent the increase in MSE when variables are removed from the CATE conditioning set. We derive efficient TE-VIM estimators which can be used with any CATE estimation strategy and are amenable to ML estimation. We propose several strategies to calculate these VIMs (e.g. leave-one out, or keep-one in), using popular meta-learners for the CATE. We study the finite sample performance through a simulation study and illustrate their application using clinical trial data.