Title: Causal Inference in the Presence of Interference
A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected may depend on who else in the population is vaccinated. In this talk we will discuss recent approaches to assessing treatment effects in the presence of interference.
Dr. Michael Hudgens is a professor in the Department of Biostatistics at UNC-Chapel Hill and is director of the Biostatistics Core of the UNC Center for AIDS Research (CFAR). He has experience in collaborative research and statistical methodology development related to studies of infectious diseases.
Professor Hudgens has co-authored more than 200 peer-reviewed papers in statistical journals such as Biometrics, Biometrika, JASA and JRSS-B as well as biomedical journals such as the Lancet, Nature and New England Journal of Medicine. He currently serves as an associate editor for Biometrics, JASA andJRSS-B. He is an elected fellow of the American Statistical Association and has taught graduate level biostatistics courses at UNC for over ten years.