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.
