Causal Inference for Data Science

This teaching line focuses on moving from predictive models to causal questions: what would happen under a different intervention, policy, design, or institutional decision?

Topics include directed acyclic graphs, confounding, matching, fixed effects, difference-in-differences, regression discontinuity, instrumental variables, sensitivity analysis, and causal machine learning.