Directed Acyclic Graphs (DAGs) have emerged as an important tool in causal modeling to understand the relationships among variables. DAGs can inform what variables should be included or excluded in a statistical model intended to minimize bias in the estimation of a causal effect. This workshop discusses the basics of DAGs used for causal inference and outlines simple rules one can follow to know which variables to include in a causal statistical model. The R package daggity will be discussed to visualize DAGs.