不要丢弃单例：存在独立流失时成对实验的高效推断方法
Don't Drop the Singletons: Efficient Inference for Pairwise Experiments with Independent Attrition
浏览论文内容
中文总结 AI 辅助
本文针对独立流失场景，提出结合特定置换检验与最优加权估计量的高效推断方法，可利用所有观测单位，功效优于配对t检验和两样本t检验，且易通过标准软件实现。
中文摘要 AI 辅助
成对随机化可在实验中带来显著效率提升，但方法学指南提醒慎用成对随机化，尤其在存在流失的场景中，部分原因是常用的估计方法（即配对固定效应）会丢弃不完整配对的数据，从而加剧流失导致的数据损失。这种丢弃不完整配对的做法，与分层精度较低的处理分配设计相比，会降低配对实验中检验的统计功效及估计量的精度。我们认为，若流失独立于处理状态和潜在结果，上述担忧是不当的，这些问题源于流失后剩余数据的低效利用。首先，我们展示如何通过使用特定的置换检验，在仍利用成对随机化设计结构的同时，将所有观测单位（完整配对及其中一个单位流失的不完整配对）用于推断，所提出的检验程序在尖锐原假设下可提供精确的尺度控制。其次，我们研究最优加权估计量，其能有效结合配对内与配对间的比较。最后，我们表明，将这两个见解结合得到的检验程序，在任意流失水平下，其功效均优于两种常用的推断方法（配对t检验和两样本t检验）。对应用研究者而言，该高效程序可通过加权固定效应回归实现，在标准软件中操作简便。综上，我们的研究为研究者提供了实用工具，使其在面对独立流失时开展成对随机化实验，无需牺牲观测值或统计功效。
英文摘要
Pairwise randomization can yield substantial efficiency gains in experiments. Yet methodological guidance cautions against pairwise randomization, especially in settings with attrition, partly because common practices for estimation (i.e., pair fixed effects) imply discarding data from incomplete pairs thus exacerbating data loss from attrition. This practice of dropping incomplete pairs reduces statistical power of tests as well as precision of estimates, in paired experiments, compared to designs with less finely stratified treatment assignment. We argue that this concern is misplaced if attrition is independent of treatment status and potential outcomes, and that these issues follow from an inefficient use of the data that remains post-attrition. First, we show how, by using a specific permutation test, it is possible to use all observed units for inference (complete pairs and incomplete pairs where one unit attrits) while still exploiting the pairwise randomization design structure. The test procedure we suggest provides exact size control under the sharp null. Second, we study an optimally weighted estimator that efficiently combines within-pair and across-pair comparisons. Finally, we show that combining these two insights yields a test procedure that dominates the two commonly used inference methods (a paired $t$-test and the two-sample $t$-test) in power, for any level of attrition. Usefully for applied researchers, we show that the efficient procedure can be implemented via a weighted fixed effects regression, straightforward in standard software. In sum, our results provide researchers with practical tools for conducting experiments with pairwise randomization without sacrificing observations or statistical power when facing independent attrition.
