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.

翻译：本文研究存在样本流失的随机对照试验中处理效应的随机化推断问题，此类试验中仅部分单元的结果可观测。我们沿用\cite{lee2009training}中报告行为的单调性假设，关注始终报告者的平均处理效应（AR-ATE），其定义为在两种处理状态下结果均可观测的单元。由于始终报告者状态仅能通过观测到的分配与响应模式部分识别，我们提出一种最坏情况随机化检验方法——该方法在所有与数据一致的始终报告者配置上最大化随机化p值，并可选择通过预检验剔除不可信的配置。利用学生化哈杰克型统计量与卡方型统计量，我们证明所得流程对严格零假设具有有限样本有效性，对弱零假设具有渐近有效性。此外，我们讨论了离散结果的计算实现方案以及基于整数规划的连续结果边界估计方法。