Advanced Topics in Management Analytics: Measuring Causal Impact

This course is part of Smith's 'Advanced Topics in Management Analytics' series, introducing Analytics alumni to causal analysis and delivered over 5 sessions/28 contact hours. Such analysis is used to measure cause and effect, whether it be the effect of a policy on the behavior of individuals or a business practice on firm profits. The course cover's modern econometric techniques that apply easily to data and allow individuals to differentiate causal relationships from mere correlations. It also focuses on various types of methods such as difference-in-differences and regression discontinuity, as well as experimental approaches such as randomized trials like A/B testing. Students will consider how causal analysis can be enhanced by machine learning, which historically has focused on prediction more than causation. The course emphasizes practical application including business-use cases. Working in teams, students complete two assignments, as well as a final summation project, leveraging the Smith School of Business team-learning model.