Randomization-based confidence sets for the local average treatment effect
针对存在不依从的随机实验，改进了Imbens & Rosenbaum (2005)的随机化推断方法，使用学生化Anderson-Rubin统计量构造置信集，在处理效应同质时精确有限样本，异质时渐近有效，并提供了高效算法。
Summary We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens & Rosenbaum (2005) has desirable properties. Specifically, we show that using a studentized Anderson–Rubin statistic as a test statistic yields confidence sets that are finite-sample exact under treatment effect homogeneity and remain asymptotically valid for the local average treatment effect when the treatment effects are heterogeneous. We provide a uniform analysis of this procedure and efficient algorithms to construct the confidence sets.