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Stratified doubly robust estimators for the average causal effect.

Satoshi Hattori1, Masayuki Henmi

  • 1Biostatistics Center, Kurume University, 67 Asahi-Machi, Kurume 830-0011, Japan.

Biometrics
|February 28, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a stratified doubly robust estimator for causal effect estimation in observational studies. It offers enhanced robustness against model misspecification compared to existing doubly robust methods.

Keywords:
Confounding factorObservational studyPropensity scoreStratification

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Area of Science:

  • Statistics
  • Epidemiology
  • Causal Inference

Background:

  • Estimating average causal effects from observational studies is crucial.
  • Doubly robust (DR) estimators combine outcome regression and propensity score weighting for improved consistency.
  • However, standard DR estimators can fail if both underlying models are misspecified.

Purpose of the Study:

  • To propose a novel stratified doubly robust (SDR) estimator.
  • To enhance robustness against model misspecification in causal effect estimation.
  • To evaluate the performance of the SDR estimator through simulations and a real-world study.

Main Methods:

  • The proposed SDR estimator integrates propensity score stratification with outcome regression and propensity score weighting.
  • It accommodates two candidate models for the propensity score.
  • Asymptotic properties were theoretically examined, and finite sample performance was assessed via simulation studies.

Main Results:

  • The SDR estimator demonstrates increased robustness; it remains consistent if the outcome regression model holds or if at least one of the propensity score models is correctly specified.
  • Simulation studies indicated favorable finite sample performance.
  • The method was applied to the Tone study, a Japanese community survey.

Conclusions:

  • The stratified doubly robust estimator offers a more reliable approach to estimating average causal effects in observational data.
  • This method provides a valuable alternative when uncertainty exists regarding the correct specification of outcome regression or propensity score models.
  • The SDR estimator enhances the reliability of causal inference in epidemiological and statistical research.