Introduction
Please note that this is a preliminary course description. The final version will be published in June 2026.
In the 21st century, information is created, digitized, and stored at unprecedented rates. The availability of high-dimensional, large-scale datasets has transformed the landscape of business analytics and data science, providing new avenues for insightful decision-making. However, vast datasets alone are insufficient for addressing fundamental analytical questions central to contemporary research and practice.
This course introduces the potential outcome framework as a rigorous foundation for causal inference. We explore microeconometric techniques designed to uncover causal relationships using experimental and quasi-experimental data. We discuss the promise and pitfalls of large-scale experimentation and consider empirical applications relevant for business and policy analysis.