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.
- Krishnamoorthy, V; Wong, DJN; Wilson, M; Raghunathan, K; Ohnuma, T; McLean, D; Moonesinghe, SR; Harris, SK
- September 2020
Volume / Issue
- 125 / 3
Start / End Page
- 393 - 397
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)