Permutation Inference with a Finite Number of Heterogeneous Clusters
提出一种简单的置换检验方法，用于在有限数量的大规模异质性聚类中检验二元处理效应的常规（非尖锐）假设，该方法通过调整显著性水平控制渐近尺寸，在至少四个处理组和四个对照组时表现良好。
Abstract I introduce a simple permutation procedure to test conventional (nonsharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjusted permutation test is easy to implement in practice and performs well at conventional levels of significance with at least four treated clusters and a similar number of control clusters. It is particularly robust to situations where some clusters are much more variable than others.