Introduction to DAGs for Causal Inference in R

Directed Acyclic Graphs (DAGs) have emerged as a valuable tool in causal modeling to understand the relationships among variables. They help identify which variables should be included or excluded from statistical models to minimize bias when estimating a causal effect. This workshop introduces the basics of DAGs in causal inference and outlines simple backdoor criterion rules to guide variable inclusion in a causal statistical model. All simulations and DAG visualizations are demonstrated in R, with brief discussion of alternative software and AI tools. Slides: https://stats.oarc.ucla.edu/r/seminars/introduction-to-dags/