Improved double-robust estimation in missing data and causal inference models

Biometrika. 2012 Jun;99(2):439-456. doi: 10.1093/biomet/ass013. Epub 2012 Apr 29.


Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory.

Keywords: Drop-out; Marginal structural model; Missing at random.