Inference with Imputed Data: The Allure of Making Stuff Up
评估了Rubin提出的随机多重插补法在缺失数据推断中的适用性，揭示了其混合贝叶斯与频率学派思想的逻辑，并检验了用随机插补替代缺失结果或协变量数据时对条件期望学习的影响。
Incomplete observability of data generates an identification problem. What one can learn about a population parameter depends on the assumptions one finds credible. Rubin has promoted random multiple imputation (RMI) as a general way to deal with missing values. The recommendation has been influential to researchers who seek a simple fix to the nuisance of missing data. This paper provides a transparent assessment of the mix of Bayesian and frequentist thinking used by Rubin to argue for RMI. It evaluates random imputation to replace missing outcome or covariate data when the objective is to learn a conditional expectation.