Title: Improved double-robust estimation in missing data and causal inference models.
Authors: Rotnitzky, Andrea; Lei, Quanhong; Sued, Mariela; Robins, James M
Published In Biometrika, (2012 Jun)
Abstract: 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.
PubMed ID: 23843666
MeSH Terms: No MeSH terms associated with this publication