Don't Drop the Singletons: Efficient Inference for Pairwise Experiments with Independent Attrition
不要丢弃单例：独立流失下配对实验的有效推断AI 解读 ↓
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不要丢弃单例：独立流失下配对实验的有效推断
- 研究问题
- 配对随机实验出现独立流失后，如何同时使用完整配对与仅剩一个单位的不完整配对，保留随机化设计的信息并提高推断效率？
- 方法与思路
- 作者提出利用全部观察单位的特定置换检验，并以最优权重结合配对内与配对间比较，给出加权固定效应回归的实现方式。
- 材料陈述的贡献
- 摘要称，尖锐零假设下检验可精确控制水平；结合加权估计的程序在所述框架中，对各种流失水平具有优于两类常用检验的功效。
- 阅读关注点
- 应核对流失独立于处理和潜在结果的假设、置换分布的构造、最优权重的依据，以及功效优势结论的模型与假设范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要的优势结论以独立流失为前提，不能延伸至处理相关或结果相关流失。
原文摘要
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.