Causal inference is one of the key challenges in the social, behavioral, and organizational sciences. Researchers invest considerable time and effort into designing their studies to validly model causal effects. Yet, the remaining limitations of their studies often pertain to causal inference, due to the complexities of social and organizational phenomena as well as various constraints faced by researchers. The purpose of this webcast is an introduction to Directed Acyclical Graphs (DAGs), a tool for causal inference that has been making inroads throughout the social sciences. We will walk through what DAGs are (and are not), the basic elements they consist of, their strengths and limitations, and how to apply them. We will discuss how DAGs can be very instructive about causal inference for researchers planning their own research, or understanding others’, by helping identify the conditions for valid causal inference given a research question. Overall, the goal is for the audience to gain a new perspective on causal inference that will help them do good research.