Doubly robust estimation of causal effect upping the odds of getting the right answers

Xiaochun Li, Changyu Shen

Research output: Contribution to journalArticle

Abstract

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.

Original languageEnglish (US)
Article numbere006065
JournalCirculation: Cardiovascular Quality and Outcomes
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • Health status
  • Odds ratio
  • Propensity score
  • Research
  • Risk

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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