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This course offers a rigorous introduction to the theory and practice of causal inference, with emphasis on real-world applications. Students will learn to estimate causal effects using both experimental and observational data, drawing on the potential outcomes framework and modern identification strategies. Topics include randomized experiments, selection bias, regression adjustment, matching methods, instrumental variables, difference-in-differences, synthetic control methods, and causal diagrams (directed acyclic graphs). The course integrates theoretical foundations with hands-on practice using real datasets, preparing students to critically evaluate and implement causal analysis in applied settings.