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.

This virtual workshop introduces participants to the basics of creating clear and effective data visualizations in Stata. Through demonstrations, example code, and hands-on exercises, participants will learn how to prepare data for visualization, create and customize common figures (including bar graphs, scatterplots, and other commonly used visualizations), and communicate findings through well-designed figures. The workshop will use Stata to demonstrate visualization techniques, but the principles for choosing, preparing, and effectively presenting visualizations are applicable across other statistical programs. This workshop is intended for students, researchers, and anyone who works with data and wants to improve their ability to present results visually. Basic familiarity with Stata is encouraged, but not required.