  We present current methods for estimating treatment effects and spillover
effects under "interference", a term which covers a broad class of situations
in which a unit's outcome depends not only on treatments received by that unit,
but also on treatments received by other units. To the extent that units react
to each other, interact, or otherwise transmit effects of treatments, valid
inference requires that we account for such interference, which is a departure
from the traditional assumption that units' outcomes are affected only by their
own treatment assignment. Interference and associated spillovers may be a
nuisance or they may be of substantive interest to the researcher. In this
chapter, we focus on interference in the context of randomized experiments. We
review methods for when interference happens in a general network setting. We
then consider the special case where interference is contained within a
hierarchical structure. Finally, we discuss the relationship between
interference and contagion. We use the interference R package and simulated
data to illustrate key points. We consider efficient designs that allow for
estimation of the treatment and spillover effects and discuss recent empirical
studies that try to capture such effects.
