The zip file linked to here contains SAS macros implementing the doubly robust approach described in:
Vansteelandt S, Carpenter J, Kenward M (2012), Analysis of incomplete data using inverse probability weighting and doubly robust estimators, Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 6, 37-48
Doubly Robust estimation implements the missing-at-random assumption by using inverse probability-of-being-observed weighting, but augmented with a model-expected value for the missing outcome. The ingenious weighted combination of these two elements has the property that the estimate of, say, treatment effect will be consistent even if one of the models – a) the model for the probability of missingness or b) the model for the value of the missing outcome – is wrong. However, the consistency property does not hold if both a) and b) are wrong. The analysis model that estimates e.g., the treatment effect, is assumed to be correct.
There is also an accompanying users guidebook explaining the macros and their use.
The macros were written by Belinda Hernández of Quintiles, with review by Michael O’Kelly.