Part 1 Conceptual foundations

Part 1 lays the essential groundwork for understanding and building causal models. Here, I’ll introduce key concepts from statistics, probabilistic modeling, generative machine learning, and Bayesian methods that will serve as our building blocks for this book’s approach to causal modeling. This part is all about arming you with the core concepts you need to start solving causal problems with machine learning tools.