Introduction to Bayesian Networks and Causal Inference in Python

How do we move beyond simple correlation to understand the actual cause-and-effect relationships in our data? Understanding causality allows you to make predictions about what will happen when you actively intervene in a system. This text-based course guides you from the absolute basics of causal reasoning to constructing and querying your own Bayesian networks using Python. You will learn how to represent complex dependencies, handle uncertainty, and perform probabilistic inference to answer real-world questions. What you will learn: 1. Understand the core principles of causal reasoning, confounding, and the difference between correlation and causation. 2. Represent causal relationships using Directed Acyclic Graphs (DAGs) and structural equations. 3. Build Bayesian networks in Python using modern probabilistic modeling libraries and clean, type-hinted code. 4. Perform probabilistic inference queries to calculate conditional probabilities and predict outcomes. 5. Apply d-separation to determine independence relationships in your data. The course begins with key terminology and foundational definitions of graphical models, transitions into practical Python implementations, and concludes with running complex inference queries. This course is designed for beginners in data science, analyst roles, or programming who want to understand probabilistic reasoning, with no prior background in causal inference required. Start reading today to master the foundations of causal modeling and probabilistic reasoning.