title: Improved double-robust estimation in missing data and causal inference models.
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.
authors.affiliation: Di Tella University, Saenz Valiente 1010, Buenos Aires 14281, Argentina , arotnitzky@utdt.edu.
authors.forename: Andrea
authors.initials: A
authors.lastname: Rotnitzky
authors.forename: Quanhong
authors.initials: Q
authors.lastname: Lei
authors.forename: Mariela
authors.initials: M
authors.lastname: Sued
authors.forename: James M
authors.initials: JM
authors.lastname: Robins
keywords: Drop-out; Marginal structural model; Missing at random