Sensitivity analysis for causal inference using inverse probability weighting

Changyu Shen, Xiaochun Li, Lingling Li, Martin C. Were

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.

Original languageEnglish (US)
Pages (from-to)822-837
Number of pages16
JournalBiometrical Journal
Volume53
Issue number5
DOIs
StatePublished - Sep 1 2011

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Keywords

  • Causal inference
  • Inverse probability weighting
  • Propensity score
  • Sensitivity analysis
  • Uncontrolled confounding

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

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