Causal Diagrams for Beginners: Map Assumptions for Better Data Analysis

Correlation is not causation, but how do you actually prove causation in your data? Causal diagrams provide a rigorous, logical framework to map your assumptions before you run a single statistical test. By learning the foundational rules of Directed Acyclic Graphs, you will transition from simply finding patterns to confidently explaining why they happen. This text-based course guides you through the core principles of causal inference, helping you identify confounding, selection bias, and measurement error through structured logical rules. What you'll learn: Understand the foundational principles of causal inference and how to translate real-world assumptions into causal diagrams; Identify common structural biases, including confounding, selection bias, and overadjustment, using simple graphical rules; Apply d-separation and back-door criteria to determine which variables to control for in your statistical models; Master the basics of modern causal software tools and text-based representation formats; Explore how causal diagrams integrate with modern data science workflows and machine learning frameworks. We begin with core definitions of causation versus association, establishing a strong conceptual foundation. From there, you will progress to constructing diagrams, analyzing complex causal pathways, and applying these structural rules to real-world data scenarios. This course is designed for beginner data analysts, researchers, and aspiring data scientists, with no prior background in advanced statistics required. Start reading today to unlock the power of causal thinking in your data analysis.