A bootstrap confidence interval procedure for the treatment effect using propensity score subclassification

Wanzhu Tu, Xiao Hua Zhou

Research output: Contribution to journalArticle

14 Scopus citations


In the analysis of observational studies, propensity score subclassification has been shown to be a powerful method for adjusting unbalanced covariates for the purpose of causal inferences. One practical difficulty in carrying out such an analysis is to obtain a correct variance estimate for inference, while reducing bias in the estimate of the treatment effect due to an imbalance in the measured covariates. In this paper, we propose a bootstrap procedure for the inferences concerning the average treatment effect; our bootstrap method is based on an extension of Efron's bias-corrected accelerated (BCa) bootstrap confidence interval to a two-sample problem. Unlike the currently available inference procedures based on propensity score subclassifications, the validity of the proposed method does not rely on aparticular form of variance estimation. A brief simulation study is included to evaluate the operating characteristics of the proposed procedure. We conclude the paper by illustrating the new procedure through a clinical application comparing the renal effects of two non-steroidal anti-inammatory drugs (NSAIDs).

Original languageEnglish (US)
Pages (from-to)135-147
Number of pages13
JournalHealth Services and Outcomes Research Methodology
Issue number2
StatePublished - Dec 1 2002


  • BCa bootstrap
  • Causal inference
  • Observational study
  • Propensity score
  • Stratification

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health

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