This workshop introduces directed acyclic graphs (DAGs) as a practical tool for selecting variables to include in analyses. Participants will learn how DAGs can help identify potential confounding and support transparent analytic decisions. Using examples from epidemiological research, the workshop will demonstrate how to develop and interpret DAGs, apply them to covariate selection, and perform sensitivity analyses to evaluate the robustness of the covariate selection process. This workshop is intended for students, researchers, and public health professionals with a basic understanding of regression modeling. No prior experience with DAGs or causal inference methods is required.

Dr. Jon Davis is an assistant professor in the Department of Occupational and Environmental Health at the University of Iowa. He earned his PhD in Epidemiology from the University of Iowa in 2017. His research focuses on injury prevention and occupational epidemiology. Dr. Davis regularly applies directed acyclic graphs (DAGs) to guide covariate selection in epidemiologic research and teaches these practical approaches to students, emphasizing transparent model development.