Causal Reasoning and Artificial Intelligence

As artificial intelligence and machine learning systems become increasingly embedded in real-world applications, they face important challenges: how to adapt to changing environments, and how to meet growing demands for interpretability, explainability, and fairness. Causal reasoning offers a promising path forward by shifting the focus from merely detecting patterns in data to understanding the underlying mechanisms of cause and effect. This expository chapter introduces the fundamental ideas and methods of causal modeling and causal discovery, with particular emphasis on graphical models as a conceptual framework and reasoning aid.