Applied Public Policy Evaluation

Many policy related empirical questions in economics are answered using detailed individual data and require analyzing individual behavior. This often implies that a causal parameters should be estimated to assess how a public policy affects individual outcomes. In many settings regression models provide correlations which only in special cases have a causal interpretation. In this course we discuss methods dealing with these confounding factors, such as omitted variables, reverse causality, measurement errors and non-random sampling. In particular, we consider limited dependent variable models, instrumental variables estimation and panel data models. We introduce the potential outcomes model, which is the most general model for defining treatment effects such as average treatment effect, average treatment effect on the treated, quantile treatment effects and local average treatment effects. The emphasis of the course is on identification, estimation and interpretation rather than a thorough treatment of the asymptotic properties of the estimators.