Bayesian Inference for the Causal Effect of Mediation

Michael J. Daniels, Jason A. Roy, Chanmin Kim, Joseph W. Hogan, Michael G. Perri

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess mediation in a recent weight management clinical trial.

Original languageEnglish (US)
Pages (from-to)1028-1036
Number of pages9
JournalBiometrics
Volume68
Issue number4
DOIs
StatePublished - Dec 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Keywords

  • Causal inference
  • Direct effect
  • Indirect effect
  • Mediators
  • Nonparametric Bayes
  • Sensitivity analysis

Fingerprint Dive into the research topics of 'Bayesian Inference for the Causal Effect of Mediation'. Together they form a unique fingerprint.

  • Cite this

    Daniels, M. J., Roy, J. A., Kim, C., Hogan, J. W., & Perri, M. G. (2012). Bayesian Inference for the Causal Effect of Mediation. Biometrics, 68(4), 1028-1036. https://doi.org/10.1111/j.1541-0420.2012.01781.x