Michael Hudgens, Ph.D.
Professor and Chair
Department of Biostatistics
Gillings School of Global Public Health
University of North Carolina
A fundamental assumption usually made in causal inference is that of no interference between individuals, i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment or exposure 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.