Directed Acyclic Graphs, or DAGs, are commonly used in Epidemiology to communicate the assumptions that are being made about the structure of the causal network being studied, and can be used to guide our analytic strategy by facilitating the identification of non-causal pathways which could bias the estimates of targeted effect estimands. The following is a list of resources that are useful in learning about DAGs and their applications:

Pearl J. Causal diagrams for empirical research. Biometrika 1995;82:669–88.

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In this article, Pearl introduces a framework utilizing DAGs to identify and communicate what assumptions are necessary to produce causal effect estimates from nonexperimental data.

Greenland S, Pearl J, Robins JM. Causal Diagrams for Epidemiologic Research. Epidemiology 1999; 10:37–48.

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Greenland, Pearl and Robins provide an introduction to the use of DAGs in Epidemiology. This includes an overview of terminology, application, and a discussion of how this framework expands the traditional epidemiologic criteria for confounding.

Glymour MM. Using causal diagrams to understand common problems in social epidemiology. Methods Soc. Epidemiol., 2006, p. 387–422.

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In Chapter 16, Glymour provides an overview for the application of DAGs in Epidemiology with an emphasis on their use in causal inference and in social epidemiology. Glymour also provides discussion of related contemporary issues such as: natural experiments, identification of direct and indirect effects, and the use of indicator variables for handling missing data.

Hernán MA, Robins JM. Graphical Representation of Causal Effects. Causal Inference What If, 2020, p. 69–82.

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Chapter 6 provides an introduction to the use of DAGs in Epidemiology for causal inference, with an emphasis on linking DAGs to the counterfactual framework.

Westreich D, Greenland S. The table 2 fallacy: Presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013; 177: 292–8.

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Westreich and Greenland use DAGs to introduce and illustrate the Table 2 Fallacy (i.e., the fallacy that all estimated coefficients for a given model can be interpreted as estimating the same type of effect). They also discuss strategies that can be employed to avoid misinterpretations that can result from the Table 2 Fallacy.

Textor J, Hardt J, Knüppel S. DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 2011;22:745.

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DAGitty is a free, user-friendly, online tool that allows you to easily build your DAG. The application is then able to identify the minimally sufficient adjustment set necessary for estimating the total effect or direct effect. It can also identify instrumental variables that are available in the causal network, and it provides a list of testable assumptions based on the causal network (http://www.dagitty.net/dags.html).