This workshop provides an introduction to causal inference using directed acyclic graphs (DAGs), a powerful framework that combines graph theory and probability to model and evaluate causal relationships. Originally developed in computer science and artificial intelligence, DAG-based methods are now widely used across disciplines such as economics, political science, sociology, health sciences, and philosophy. The course introduces the theoretical foundations of causal data science while emphasizing practical applications for empirical research. Participants will learn how to address common causal inference challenges – including backdoor adjustment, bad controls, instrumental variables, selection bias, and external validity – within a unified graphical framework. Through hands-on exercises and applications in R, the course equips participants with the tools to apply DAG-based causal reasoning and modern identification techniques in their own research projects. No formal prerequisites are required, although familiarity with basic statistics and R is beneficial.
Internal event