Causal inference in perioperative medicine observational research: part 1, a graphical introduction. (September 2020)

• Record Type: Journal Article
• Authors: Krishnamoorthy, Vijay; Wong, Danny J.N.; Wilson, Matt; Raghunathan, Karthik; Ohnuma, Tetsu; McLean, Duncan; Moonesinghe, S. Ramani; Harris, Steve K.
• Is Part Of: British journal of anaesthesia. Volume 125:Number 3(2020)
• Page Start: 393
• Page End: 397
• Publication Date: 2020-09
• Subjects: causal inference -- confounding -- epidemiology -- graphical models -- observational research; Anesthesiology -- Periodicals; Anesthesia -- Periodicals; 617.9605
• DOI: 10.1016/j.bja.2020.03.031
• Languages: English
• ISSNs: 0007-0912

Abstract

Abstract: 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.