Nonparametric tests of treatment effect homogeneity for policy-makers

TL;DR

This paper introduces nonparametric tests for treatment effect heterogeneity, enabling policy-makers to assess personalized treatment impacts without relying on parametric assumptions.

Contribution

It develops a flexible class of nonparametric tests that handle various covariate types and structured assumptions, aiding treatment policy decisions.

Findings

Tests effectively detect heterogeneity in simulations.

Re-analysis of AIDS trial demonstrates practical utility.

Abstract

Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null distribution. Furthermore, we show how the tests are tailored to detect alternatives where the population impact of adopting a personalized decision rule differs from using a rule that discards covariates. The proposal is thus relevant for guiding treatment policies. The utility of the proposal is borne out in simulation studies and a re-analysis of an AIDS clinical trial.