This course introduces advanced concepts and methods for causal inference from data. The course will introduce students to both the statistical theory and practice behind making causal inferences. Topics covered in the course include causal identification, the g-formula, potential outcomes, experiments, matching, regression discontinuity designs, g-estimation of structural nested models, causal mediation analysis, methods to handle unmeasured confounding, inverse probability weighting of marginal structural models, difference-in-differences, instrumental variables estimation, sensitivity analysis, dynamic causal inference, and more. The course will draw upon examples from social sciences.
Baiocchi, M., Small, D, Lorch, S., & Rosenbaum, P. (2010). Building a stronger instrument in an observational study of perinatal care for premature infants. Journal of the American Statistical Association, 105(492), 1285—1296.