Intended as a continuation of API-201, this course equips students with an understanding of common tools of empirical analysis in policy applications, with a particular emphasis on causal inference. Much of the learning will take place through hands-on analysis of data sets, as well as case studies illustrating critical concepts. The course will cover the mechanics of regression analysis, including multiple regression, dummy variables, and binary dependent variables; as well as program evaluation, including observational, experimental, and quasi-experimental techniques. The final part of the course includes an integrative exercise in which students will have the opportunity to assess empirical analysis in an open-ended and professionally realistic project.
Prerequisite: API-201 or equivalent. May not be taken for credit with API-210.