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Deconfounding confounding part 2: using directed acyclic graphs (DAGs)

One of the few true innovations in epidemiological methods has been the emergence of directed acyclic graphs (DAGs) to identify confounding. This development began in the early 1990s with work by Pearl and Robins based on formal logic and machine learning. DAGs are a formal system of mapping variables and the direction of causal relationships among them. “Directed” refers to arrows indicating the direction of causality between variables, and “acyclic” means that it should not be possible to start from any one variable and follow a series of arrows back to the original variable. Entire books are devoted to this method, but just a few highlights are sufficient to help clinicians understand confounding.