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Randomization Inference For the Always-Reporter Average Treatment Effect

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  • Haoge Chang
  • Zeyang Yu

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.

Suggested Citation

  • Haoge Chang & Zeyang Yu, 2026. "Randomization Inference For the Always-Reporter Average Treatment Effect," Papers 2603.24970, arXiv.org, revised Mar 2026.
    • Handle: RePEc:arx:papers:2603.24970
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    References listed on IDEAS

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