Causal Inference and DAGs in R

Understanding the difference between correlation and causation is a critical skill for any modern data professional. This course provides a structured path to mastering causal reasoning, enabling you to draw valid conclusions from complex datasets rather than just identifying patterns. You will gain a solid foundation in causal logic, moving from basic definitions to the practical application of Directed Acyclic Graphs (DAGs). By the end of the course, you will be able to build, visualize, and interpret causal models that inform better decision-making in fields ranging from finance to health sciences. What you'll learn: - Understand the fundamental principles of causal logic and graph theory - Construct Directed Acyclic Graphs to visualize complex variable relationships - Identify and control for confounding variables and selection bias - Apply modern identification algorithms to automate causal discovery tasks - Implement causal models using current R libraries and modern coding practices - Interpret the results of causal analysis to provide actionable insights The course begins with essential terminology and the logic of causality before moving into practical modeling and implementation techniques using R. It is designed for beginners in data science and analytics, and while some familiarity with basic statistics is helpful, no prior experience with causal inference is required. Start building more reliable and interpretable data models today.