Abstract: 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.
About the speaker: Dr. Michael Hudgens is Professor and Chair of the Department of Biostatistics at the University of North Carolina at Chapel Hill. He also serves as Director of the Biostatistics Core of the UNC Center for AIDS Research (CFAR). His work focuses on collaborative research and the development of statistical methodology, particularly in studies of infectious diseases. Professor Hudgens has co-authored more than 300 peer-reviewed publications in leading statistical journals such as Biometrics, Biometrika, JASA, and JRSS-B, as well as in major biomedical journals including The Lancet, Nature, and the New England Journal of Medicine. He currently serves as an Associate Editor for Biometrics and JASA. He is an elected Fellow of the American Statistical Association and has taught graduate-level biostatistics courses at UNC for over 20 years.
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