Graduate Workshop in Graphing Causality: Digging the Directed Acyclic Graphs (DAGs)

  1. Chin-Chi Kuo (China Medical University, Taiwan)
  2. Hsiu-Yin Chiang (China Medical University, Taiwan)
  3. Tsung Yu (National Cheng-Kung University, Taiwan)
Brief Intro:

Like sailors, who need the visibility to navigate their ship on the right course, researchers need guiding maps to indicate where the biases are in the causal pathway and safely past obstacles threatening the validity of casual inferences. To meet the need for this guiding map, directed acyclic graphs (DAGs) provide a handy tool to help researchers identify and visualize potential variables with significant causal influences.

  1. Understand DAGs should be an integral part of epidemiological research
  2. Understand terminology and operational definitions of DAGs
  3. Be able to draw and use DAGs as a framework for evaluating exposure-outcome relationship
  4. Be able to use DAGs to visualize residual confounding and potential over-adjustment
  5. Understand limitations of DAGs