  Incomplete observability of data generates an identification problem. There
is no panacea for missing data. What one can learn about a population parameter
depends on the assumptions one finds credible to maintain. The credibility of
assumptions varies with the empirical setting. No specific assumptions can
provide a realistic general solution to the problem of inference with missing
data. Yet Rubin has promoted random multiple imputation (RMI) as a general way
to deal with missing values in public-use data. This recommendation has been
influential to empirical researchers who seek a simple fix to the nuisance of
missing data. This paper adds to my earlier critiques of imputation. It
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. It considers steps that might help combat the allure
of making stuff up.
