Causal inference in perioperative medicine observational research: part 1, a graphical introduction. (September 2020) - Krishnamoorthy, Vijay; Wong, Danny J.N.; Wilson, Matt; Raghunathan, Karthik; Ohnuma, Tetsu; McLean, Duncan; Moonesinghe, S. Ramani; Harris, Steve K.
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.