The beginnings of the course focuses on the role of quantitative methods in public policy analysis and evaluation and theoretical considerations behind (correlation versus causality; the potential outcomes and the Rubin causal model; experiments and quasi-experiments versus observations; internal/external validity). After this, the week is more applied with practical examples and replication of published papers. The objective is in showing how regression-based methods provide an empirical framework to test for “causal effect” when data come from observations rather than experiments. The key point will be to learn the participants why and how the regression-based methods are used to identify the so-called “treatment effect”, in other words to prove that the policy has or not the expected effects. Methods like the “comparison of means across groups” (“treated group/control group“), difference in differences”, “instrumental variable” or “regression discontinuity design” will be covered and presented in easy formalization, with practical lab sessions. The course is introductory/intermediate