Directed acyclic graphs - The view of a clinical scientist

Abstract

The three objectives of this talk were to

  1. Understand the basics to operationalize Directed Acyclic Graphs (DAGs)
  2. Appreciate the insights into confounding and selection bias provided by DAGs
  3. Examine examples to appreciate the importance of DAGs (and their encoded substantive knowledge) on the road to causal inference

My take home messages from this presentation were;

  1. Causal interpretations are not possible by uniquely examing statistical associations
  2. Causal interpretations can only be made by the sensible inclusion of external judgement or evidence
  3. DAGs help avoid common biases including confounding and selection bias
  4. DAGs help identify causal effects by deriving testable implications of a causal model

Date
Nov 3, 2021 12:00 PM — 1:00 PM
Location
McGill University Hospital Center
Montreal, QC
Professor of Medicine & Epidemiology

I am a tenured (full) professor with a joint appointment in the Departments of Medicine and Epidemiology and Biostatistics where I work as a clinical cardiologist and do research in cardiovascular epidemiology.