This course continues the sequence in statistical modeling. It assumes prior knowledge of simple and multiple linear regression modelling and introduces students to a new perspective of studying causes and effects in social science research. Based on a framework of causality, the course agenda covers various strategies for uncovering causal relationships using statistical tools.
We start with reflecting about causality and the ideal research design and then learn to use a framework for studying causal effects. Then, we revisit common regression estimators of causal effects and learn about their limits. Next, we focus on instrumental variables, panel and difference-in-differences estimators, regression discontinuity designs and techniques for exploring moderated and mediated relationships. All classes take place in the computer lab and the time is divided between theory and application. Students are assigned a problem set at the end of each class covering that day’s materials.
Please note that Statistics II in the Fall Semester is open to MPP students who have a waiver for Statistics I as well as to 2nd year MPP (management and organisation track) and MIA students who are interested in deepening their statistics knowledge after having completed Statistics I. In the Spring Semester the course is open to MPP 1st year students in the policy analysis track only.