• Br J Anaesth · Sep 2020

    Review

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

    • Vijay Krishnamoorthy, Wong Danny J N DJN Department of Anaesthesia, Guy's and St Thomas' NHS Foundation Trust, London, UK., Matt Wilson, Karthik Raghunathan, Tetsu Ohnuma, Duncan McLean, S Ramani Moonesinghe, and Steve K Harris.
    • Critical Care and Perioperative Epidemiologic Research (CAPER) Unit, Department of Anesthesiology, Duke University Hospital, Durham, NC, USA. Electronic address: vijay.krishnamoorthy@duke.edu.
    • Br J Anaesth. 2020 Sep 1; 125 (3): 393-397.

    AbstractGraphical 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.Copyright © 2020 British Journal of Anaesthesia. All rights reserved.

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