Date: 2015-03-13

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Despite almost universal acceptance across most fields of statistics, Bayesian inferential methods have yet to breakthrough to widespread use in causal inference, despite Bayesian arguments being a core component of early developments in the field. Some quasi-Bayesian procedures have been proposed, but often these approaches rely on heuristic, sometimes flawed, arguments. In this talk I will discuss some formulations of classical causal inference problems from the perspective of standard Bayesian representations, and propose some inferential solutions. This is joint work with Olli Saarela, Dalla Lana School of Public Health, University of Toronto, Erica Moodie, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, and Marina Klein, Division of Infectious Diseases, Faculty of Medicine, McGill University.

Speaker

David A. Stephens ia a James McGill Professor in the Department of Mathematics and Statistics at McGill University.