How to use directed acyclic graphs: guide for clinical researchers

Directed acyclic graphs are commonly used to illustrate and assess the hypothesised causal mechanisms in health and social research. These graphs can illuminate investigators' assumptions and help clearly describe each possible explanation for associations observed in data given researchers' assumptions, ranging from causal effects to confounding and selection bias, and thereby help identify variables that can be used to reduce or overcome bias. This article explains how to construct, interpret, and present directed acyclic graphs as part of clinical research studies and how they can help communicate a study's strengths or limitations.