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

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.

Duke Scholars

Author Vijay Krishnamoorthy Anesthesiology, Critical Care Medicine

Author Karthik Raghunathan Anesthesiology, VA Anesthesiology Service

Author Tetsu Ohnuma Anesthesiology, Critical Care Medicine