Foundations of Causal Inference for Data-Driven Decisions

4.0 credits · Graduate

This course provides a rigorous introduction to the theory and practice of causal inference for data-driven decision-making. Students will learn to identify and estimate causal effects using both experimental and observational data, combining statistical foundations with computational tools. Emphasis is placed on understanding causal assumptions, designing valid estimators, and analyzing the theoretical properties of each method. Attention will be given to unique challenges that arise in real-world operational and policy decisions. Example applications include online marketplaces, social networks, and inventory control. Topics include the potential outcomes framework, causal diagrams, randomized and quasi-experimental designs, matching and weighting methods, instrumental variables, difference-in-differences, and introductory causal machine learning.