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Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows connecting nodes that represent variables and in doing so can demonstrate the causal relation between different variables. cDAGs can provide researchers with a blueprint of the exposure and outcome relation and the other variables that play a role in that causal question. cDAGs can be helpful in the design and interpretation of observational studies in pulmonary, critical care, sleep, and cardiovascular medicine. They can also help clinicians and researchers to better identify the structure of different biases that can affect the validity of observational studies. Most of the available literature on cDAGs and their function use language that might be unfamiliar to clinicians. This article explains cDAG terminology and the principles behind how they work. We use cDAGs and clinical examples that are mostly focused in the area of pulmonary medicine to describe the structure of confounding, selection bias, overadjustment bias, and detection bias. These principles are then applied to a more complex published case study on the use of statins and COPD mortality. We also introduce readers to other resources for a more in-depth discussion of causal inference principles.

Original publication




Journal article



Publication Date





S21 - S28


causal directed acyclic graphs, colliders, confounding, detection bias, overadjustment bias, selection bias, Bias, Biomedical Research, Causality, Epidemiologic Studies, Humans, Observational Studies as Topic, Research Design