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

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  • Andrea Rotnitzky
  • Quanhong Lei
  • Mariela Sued
  • James M. Robins

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.

Suggested Citation

  • Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:2:p:439-456
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    File URL: http://hdl.handle.net/10.1093/biomet/ass013
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