A Nonparametric Test of Heterogeneous Treatment Effects under Interference

TL;DR

This paper introduces nonparametric statistical methods to test for heterogeneous treatment effects in populations with interference, accounting for interactions and exposure variables, and demonstrates their application on real experimental data.

Contribution

It develops kernel-based tests for HTEs under interference, incorporating clustered interference into potential outcomes, and provides a multiple-testing approach to identify heterogeneity sources.

Findings

Proposed test statistics have proven asymptotic properties.

Applied methods successfully to real-world weather insurance data.

Disentangled effects of treatment assignment and exposure variables.

Abstract

Statistical inference of heterogeneous treatment effects (HTEs) across predefined subgroups is challenging when units interact because treatment effects may vary by pre-treatment variables, post-treatment exposure variables (that measure the exposure to other units' treatment statuses), or both. Thus, the conventional HTEs testing procedures may be invalid under interference. In this paper, I develop statistical methods to infer HTEs and disentangle the drivers of treatment effects heterogeneity in populations where units interact. Specifically, I incorporate clustered interference into the potential outcomes model and propose kernel-based test statistics for the null hypotheses of (i) no HTEs by treatment assignment (or post-treatment exposure variables) for all pre-treatment variables values and (ii) no HTEs by pre-treatment variables for all treatment assignment vectors. I recommend a multiple-testing algorithm to disentangle the source of heterogeneity in treatment effects. I prove the asymptotic properties of the proposed test statistics. Finally, I illustrate the application of the test procedures in an empirical setting using an experimental data set from a Chinese weather insurance program.