Testing Treatment Effect Heterogeneity with Functional Outcomes

Abstract

Assessing treatment-effect heterogeneity is important in causal inference, particularly for functional outcomes where effects may vary over time and the common effect is unknown. We propose a nonparametric procedure that assesses treatment-effect homogeneity through a testable marginal implication. By comparing within-arm difference distributions, the construction removes the unknown common functional effect without estimating it. We measure departures between these distributions through an integrated squared discrepancy of their characteristic functionals. A full-support Gaussian integrating measure yields an explicit bounded-kernel representation and identifies equality of the difference laws without requiring moment conditions on the outcomes. We construct an unbiased generalized U-statistic that accounts for dependence among overlapping differences, derive its null, fixed-alternative, and local-alternative limits, and develop a centered multiplier bootstrap for inference. The framework extends to multiple treatment arms with a shared control. For computation, a random-direction approximation combined with a nine-state recursion reduces the cost to linear order in the sample size for fixed numerical settings. Simulation studies assess the finite-sample performance of the proposed procedure relative to several benchmark methods. An application to the Irish gas smart-metering trial illustrates its use for assessing heterogeneity in functional treatment responses.