Causal Inference: from Data to Decisions

Catalog description

Every day, you're bombarded with bold claims on social media and TV—flashy ads promising "miracle supplements" that "burn fat fast," or viral posts wielding suspicious bar graphs to sway your opinion ("Look at how crime spikes when immigration spikes!"). This course equips you with the analytical tools to see through the hype, evaluate the evidence, and distinguish genuine causal relationships from cleverly disguised BS. You will learn the methods economists use to study causal relationships, beginning with linear regression—the fundamental tool for identifying patterns—and progressing to advanced econometric techniques such as difference-in-differences, regression discontinuity designs, and instrumental variables. Through hands-on coding labs and a flipped classroom model, you will gain practical experience in data analysis while learning how to frame a research question, establish the criteria for causality, and interpret your findings with confidence. Later, you will critically evaluate the causal arguments in real research by critiquing studies and presenting your insights in a polished group report. This course is designed not only to teach you that "correlation is not causation" but also to show you why that is the case and when causal claims are justified. Prerequisites: 36-202 or 85-309 or 70-208 or 36-309