In recent years, causal inference has become essential for data-driven decision making, as these methods can protect against biases in traditional statistical modeling techniques. In this course, students will learn how to use various methods to draw causal inferences through practical experience and real-world data examples in areas such as policy, marketing and operations. Topics covered will include randomized A/B experiments, difference-in-differences, instrumental variables, and modern machine learning/AI tools.
It is recommended that you have taken BUS 41001 (or 41100) or an equivalent class on basics of regression methods. This prerequisite is not strictly enforced but the lecture material may sometimes assume knowledge of standard regression analysis concepts (such as standard errors or model selection). Undergraduates require instructor permission. Cannot enroll if BUSN 41207 taken previously.
Description and/or course criteria last updated: July 01 2026
