In this paper we revisit some common recommendations regarding the analysis of matched-pair and stratified experimental designs in the presence of attrition. Our main objective is to clarify a number of well-known claims about the practice of dropping pairs with an attrited unit when analyzing matched-pair designs. Contradictory advice appears in the literature about whether or not dropping pairs is beneficial or harmful, and stratifying into larger groups has been recommended as a resolution to the issue. To address these claims, we derive the estimands obtained from the difference-in-means estimator in a matched-pair design both when the observations from pairs with an attrited unit are retained and when they are dropped. We find limited evidence to support the claims that dropping pairs helps recover the average treatment effect, but we find that it may potentially help in recovering a convex weighted average of conditional average treatment effects. We report similar findings for stratified designs when studying the estimands obtained from a regression of outcomes on treatment with and without strata fixed effects.

翻译：本文重新审视了在存在缺失数据情况下分析配对和分层实验设计的一些常用建议。我们的主要目标是澄清关于在分析配对设计时删除存在缺失数据配对的若干众所周知的说法。文献中关于删除配对是否有益或有害的建议相互矛盾，并且建议将分层划分为更大的组作为解决该问题的方法。为应对这些说法，我们推导了在保留和删除存在缺失数据配对观测值时，配对设计中均值差异估计量所得的估计目标。我们发现，支持删除配对有助于恢复平均处理效应的证据有限，但发现这有可能有助于恢复条件平均处理效应的凸加权平均值。当研究有无分层固定效应的处理结果回归所得的估计目标时，我们在分层设计中报告了类似发现。