Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data
研究了随机实验中协变量和结果数据缺失时，如何在不做不可检验假设的情况下，对总体参数进行边界估计，并推导了协变量完全随机缺失时的边界，应用于临床试验和家庭结构与高中毕业率关系的数据。
Abstract Analysis of randomized experiments with missing covariate and outcome data is problematic, because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This article shows how population parameters can be bounded without making untestable distributional assumptions. Bounds are also derived under the assumption that covariate data are missing completely at random. In each case the bounds are sharp; they exhaust all of the information available given the data and the maintained assumptions. The bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school.