Quantitative Methods for Public Policy Analysis
This course introduces participants to major basic concepts and tools for quantitative evaluation of public policies. Among the many concepts used by specialists of causal reasoning and policy evaluation, the following will be clarified: counterfactual, potential outcomes, quasi-experiment, treatment effect, and before-after effect. The course will show how statistical methods can be useful to address problems and to create a formal framework for quasi-experimental reasoning. The basics of regression models will be presented in relationship to testing the effect of « treatment » (like a policy change or to be exposed to political reforms).
Building on the first week, which delivers fundamentals of regression methods for policy analysis, the second week provides a survey of more advanced empirical tools for political science and public policy research. The focus is on statistical methods for causal inference, i.e. methods designed to address research questions that concern the impact of some potential cause (an intervention, a change in institutions, economic conditions, or policies) on some outcome ( vote choice, income, election results, crime rates, etc).
Students will have the opportunity to replicate the examples and exercises elaborated during the Summer School, and apply the techniques on their own research and personal work.
Prior knowledge of some basic statistics is recommended for this course.