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 depends 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. Inference about different direct and indirect (or spillover) effects will be considered in a population where individuals form groups such that interference is possible between individuals within the same group but not between individuals in different groups.