This paper develops a unified framework for partial identification and inference in stratified experiments with attrition, accommodating both equal and heterogeneous treatment shares across strata. For equal-share designs, we apply recent theory for finely stratified experiments to Lee bounds, yielding closed-form, design-consistent variance estimators and properly sized confidence intervals. Simulations show that the conventional formula can overstate uncertainty, while our approach delivers tighter intervals. When treatment shares differ across strata, we propose a new strategy, which combines inverse probability weighting and global trimming to construct valid bounds even when strata are small or unbalanced. We establish identification, introduce a moment estimator, and extend existing inference results to stratified designs with heterogeneous shares, covering a broad class of moment-based estimators which includes the one we formulate. We also generalize our results to designs in which strata are defined solely by observed labels.

翻译：本文建立了一个统一框架，用于处理存在流失现象的分层实验中的部分识别与推断问题，该框架同时适用于各层间处理份额相等和异质的情况。对于等份额设计，我们将精细分层实验的最新理论应用于李（Lee）界，得到了封闭形式的、设计一致的方差估计量以及尺度恰当的置信区间。模拟结果表明，传统公式可能高估不确定性，而我们的方法能提供更紧致的区间。当各层处理份额不同时，我们提出了一种新策略，该策略结合逆概率加权与全局截断方法，即使在层规模较小或不平衡的情况下也能构建有效的界。我们确立了识别条件，引入了一种矩估计量，并将现有的推断结果推广到具有异质份额的分层设计中，涵盖了一类广泛的基于矩的估计量，其中包括我们构建的估计量。我们还将结果推广到仅通过观测标签定义层别的设计情形。