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The course is on identification and estimation of causal effects.
It will cover identification of causal parameters and the missing counterfactual problem, average treatment on the treated effect (ATT), average intent to treat effect (AIT), spillovers and average indirect treatment effects (ITE) as well as randomized control trials (RTC) and regression discontinuity design (RDD), instrumental variables (IV), and local average treatment effects (LATE). Students will also learn about general issues in estimation of non-linear models. GMM, ML, invertibility, simulation methods, numeric optimization, endogeneity, limited dependent variable models.