This course introduces causal inference and AI-enabled computational methods for studying social phenomena, institutional behaviour and policy problems. It begins with philosophical and scientific accounts of causation, then connects them to causal reasoning in experiments, economics, public policy and legal evidence. The main body of the course examines two influential traditions: Rubin's potential-outcomes framework, including randomised controlled trials, propensity scores, matching, instrumental variables, regression discontinuity, difference-in-differences and causal machine learning; and Pearl's causal-graph framework, including Bayesian networks, directed acyclic graphs, structural causal models, d-separation, do-calculus and counterfactual reasoning. The course then introduces selected AI and computational approaches, including Double Machine Learning, heterogeneous treatment effects, computational text analysis, agent-based modelling, adaptive experiments and responsible AI. Through interdisciplinary examples and Python-based tutorials, students learn to formulate causal questions, evaluate identification assumptions, implement selected methods and assess the interpretability, ethical limits and policy relevance of computational evidence.