Inference with Imputed Data: The Allure of Making Stuff Up

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.