Bounded, efficient and doubly robust estimation with inverse weighting

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Abstract

Consider estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on a previous nonparametric likelihood approach and propose new doubly robust estimators, which have desirable properties in efficiency if the propensity score model is correctly specified, and in boundedness even if the inverse probability weights are highly variable. We compare the new and existing estimators in a simulation study and find that the robustified likelihood estimators yield overall the smallest mean squared errors.

Original languageEnglish (US)
Pages (from-to)661-682
Number of pages22
JournalBiometrika
Volume97
Issue number3
DOIs
StatePublished - Sep 1 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Causal inference
  • Double robustness
  • Inverse weighting
  • Missing data
  • Nonparametric likelihood
  • Propensity score

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