Causal inference in perioperative medicine observational research: part 1, a graphical introduction.

Journal Article (Journal Article;Review)

Graphical models have emerged as a tool to map out the interplay between multiple measured and unmeasured variables, and can help strengthen the case for a causal association between exposures and outcomes in observational studies. In Part 1 of this methods series, we will introduce the reader to graphical models for causal inference in perioperative medicine, and set the framework for Part 2 of the series involving advanced methods for causal inference.

Full Text

Duke Authors

Cited Authors

  • Krishnamoorthy, V; Wong, DJN; Wilson, M; Raghunathan, K; Ohnuma, T; McLean, D; Moonesinghe, SR; Harris, SK

Published Date

  • September 2020

Published In

Volume / Issue

  • 125 / 3

Start / End Page

  • 393 - 397

PubMed ID

  • 32600803

Electronic International Standard Serial Number (EISSN)

  • 1471-6771

Digital Object Identifier (DOI)

  • 10.1016/j.bja.2020.03.031


  • eng

Conference Location

  • England