Understanding and Conducting Business Experiments

3 units

This course introduces the essential methods of A/B testing and causal inference, which are key components of modern data science. Success in the age of AI requires more than just finding patterns in data. We need to understand the underlying processes that generate those patterns. In fact, the range of business and policy questions that require causal inference is broader than commonly recognized, including high-demand topics like churn prediction, digital marketing strategy, and product development. The course also supports students in developing the necessary programming skills to conduct these analyses. By the end of the term, students will possess the analytical toolkit to distinguish causal relationships from simple correlations, enhancing their data-driven decision-making.