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

• 成果类型: Article
• 署名作者: Rotnitzky, Andrea; Lei, Quanhong; Sued, Mariela; Robins, James M.
• 署名单位: Universidad Torcuato Di Tella; University of Buenos Aires; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass013
• 发表日期: 2012
• 页码: 439456
• 关键词: LIKELIHOOD; EFFICIENCY

摘要

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.