标题： 随机化推断与样本流失 显示英文标题

标题： Randomization Inference with Sample Attrition

摘要： 尽管具有吸引力，但由于样本流失，处理效应的随机化推断可能会出现严重的大小偏差。 我们提出了新的、计算效率高的随机化推断方法，在一系列可能有信息的缺失机制下仍然有效。 我们首先通过在所有可能的缺失结果插补中对 Fisher 随机化检验的最坏情况 p 值进行构造，来为检验严格零假设构建有效的 p 值。 利用无分布检验统计量，这种最坏情况 p 值可以得到闭式解，自然地与部分识别文献中的界限相连接。 我们的检验统计量结合了潜在结果和缺失性指示变量，使我们能够利用结构假设——如单调缺失——来提高功效。 我们将框架进一步扩展到检验关于个体处理效应分位数的非严格零假设。 这些方法通过模拟和一个实证应用进行了说明。 显示英文摘要

摘要： Although appealing, randomization inference for treatment effects can suffer from severe size distortion due to sample attrition. We propose new, computationally efficient methods for randomization inference that remain valid under a range of potentially informative missingness mechanisms. We begin by constructing valid p-values for testing sharp null hypotheses, using the worst-case p-value from the Fisher randomization test over all possible imputations of missing outcomes. Leveraging distribution-free test statistics, this worst-case p-value admits a closed-form solution, connecting naturally to bounds in the partial identification literature. Our test statistics incorporate both potential outcomes and missingness indicators, allowing us to exploit structural assumptions-such as monotone missingness-for increased power. We further extend our framework to test non-sharp null hypotheses concerning quantiles of individual treatment effects. The methods are illustrated through simulations and an empirical application.