Andrew Gelman, Statistics and Political Science, Columbia University
Wed 27 Mar, 4pm, Betty Ford Auditorium, Ford School of Public Policy
Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification yet must learn from available information.