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

Andrea Rotnitzky, Quanhong Lei, Mariela Sued and James M. Robins

Biometrika, 2012, vol. 99, issue 2, 439-456

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. Copyright 2012, Oxford University Press.

Date: 2012
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