Randomization Inference For the Always-Reporter Average Treatment Effect
Abstract
This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume monotonicity in reporting behavior as in \cite{lee2009training} and focus on the average treatment effect for always-reporters (AR-ATE), defined as units whose outcomes are observed under both treatment and control. Because always-reporter status is only partially revealed by observed assignment and response patterns, we propose a worst-case randomization test that maximizes the randomization p-value over all always-reporter configurations consistent with the data, with an optional pretest to prune implausible configurations. Using studentized Hajek- and chi-square-type statistics, we show the resulting procedure is finite-sample valid for the sharp null and asymptotically valid for the weak null. We also discuss computational implementations for discrete outcomes and integer-programming-based bounds for continuous outcomes.
Cite
@article{arxiv.2603.24970,
title = {Randomization Inference For the Always-Reporter Average Treatment Effect},
author = {Haoge Chang and Zeyang Yu},
journal= {arXiv preprint arXiv:2603.24970},
year = {2026}
}