A Bayesian approach to the g-formula

Alexander P. Keil, Eric J. Daza, Stephanie M. Engel, Jessie P. Buckley, Jessie K. Edwards

Research output: Contribution to journalArticlepeer-review


Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.

Original languageEnglish (US)
Pages (from-to)3183-3204
Number of pages22
JournalStatistical Methods in Medical Research
Issue number10
StatePublished - Oct 1 2018
Externally publishedYes


  • Bayesian
  • causal inference
  • g-computation
  • semiparametric

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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