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Incomplete observability of data generates an identification problem. Thereis no panacea for missing data. What one can learn about a population parameterdepends on the assumptions one finds credible to maintain. The credibility ofassumptions varies with the empirical setting. No specific assumptions canprovide a realistic general solution to the problem of inference with missingdata. Yet Rubin has promoted random multiple imputation (RMI) as a general wayto deal with missing values in public-use data. This recommendation has beeninfluential to empirical researchers who seek a simple fix to the nuisance ofmissing data. This paper adds to my earlier critiques of imputation. Itprovides a transparent assessment of the mix of Bayesian and frequentistthinking used by Rubin to argue for RMI. It evaluates random imputation toreplace missing outcome or covariate data when the objective is to learn aconditional expectation. It considers steps that might help combat the allureof making stuff up.

Inference with Imputed Data: The Allure of Making Stuff Up