STAT 451 - Causal Inference

“Correlation does not imply causation.” We’ve all heard this mantra, warding us away from reading too much into the association between murder rates and ice cream sales, between shoe size and reading ability, and the like. But this mantra leaves us wanting: how do we study causation? Questions of causation are essential when we try to understand the effects of new medical treatments, policies, or programs. In this course, we’ll examine frameworks of thinking, statistical tools, and study designs that enable us to learn about the causal effects of interventions. Some specific topics include graphical causal models, randomized experiments, propensity score methods, instrumental variables, and interrupted time series designs. This course should be useful to those interested in biology, economics, medicine, public policy and any area in which interventions are routinely evaluated. Prerequisite(s): STAT 155 and STAT 354 Offered occasionally. (4 Credits)