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随机对照试验中配对设计的最优性

Optimality of Matched-Pair Designs in Randomized Controlled Trials
American Economic Review · 2022 · Yuehao Bai

中文摘要

在随机对照试验中,处理通常通过分层随机化进行分配。本文表明,在所有以二分之一的概率处理所有单元的分层随机化方案中,某种配对设计在估计平均处理效应时实现了最大的统计精度。在一个重要的特殊情形下,最优设计根据基线结果对单元进行配对。在一项基于十项随机对照试验数据集的模拟研究中,与原有设计相比,该设计使平均处理效应估计量的标准误平均降低了10%,最多降低了34%。(JEL C13, C21)

Abstract

In randomized controlled trials, treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes that treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect. In an important special case, the optimal design pairs units according to the baseline outcome. In a simulation study based on datasets from ten randomized controlled trials, this design lowers the standard error for the estimator of the average treatment effect by 10 percent on average, and by up to 34 percent, relative to the original designs. (JEL C13, C21)
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