On Mitigating the Analytical Limitations of Finely Stratified Experiments
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缓解精细分层实验的分析局限
- 研究问题
- 在配对等精细分层实验中，处理效应跨层异质会使传统方差估计偏保守，如何利用观测协变量改善这种推断？
- 方法与思路
- 作者借助经典最小二乘理论构造新的方差估计量，用协变量解释效应异质性，并据此提出恒定处理效应的检验。
- 材料陈述的贡献
- 摘要称新估计量在期望上仍保守，但渐近保守程度不超过传统方法；改善幅度取决于协变量对效应异质性的解释能力。
- 阅读关注点
- 应核对协变量调整的具体形式、保守性与渐近比较的条件，以及超总体模型中将协变量视为固定对结论的影响。
- 阅读边界
- 仅依据所给 Crossref 摘要；具体正则条件、有限样本表现及可推广的超总体模型未说明，不能推断其他版本的变化。
原文摘要
Summary Although attractive from a theoretical perspective, finely stratified experiments such as paired designs suffer from certain analytical limitations that are not present in block-randomized experiments with multiple treated and control individuals in each block. In short, when using a weighted difference in means to estimate the sample average treatment effect, the traditional variance estimator in a paired experiment is conservative unless the pairwise average treatment effects are constant across pairs; however, in more coarsely stratified experiments, the corresponding variance estimator is unbiased if treatment effects are constant within blocks, even if they vary across blocks. Using insights from classical least squares theory, we present an improved variance estimator that is appropriate in finely stratified experiments. The variance estimator remains conservative in expectation but is asymptotically no more conservative than the classical estimator and can be considerably less conservative. The magnitude of the improvement depends on the extent to which effect heterogeneity can be explained by observed covariates. Aided by this estimator, a new test for the null hypothesis of a constant treatment effect is proposed. These findings extend to some, but not all, superpopulation models, depending on whether the covariates are viewed as fixed across samples.