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.

中文翻译：

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实验数据中的溢出效应

我们介绍了在“干扰”下估计治疗效果和溢出效应的当前方法，该术语涵盖了一大类情况，其中一个单位的结果不仅取决于该单位接受的治疗，还取决于其他单位接受的治疗。就各单位相互反应、相互作用或以其他方式传递治疗效果而言，有效推理要求我们考虑此类干扰，这与传统假设不同，即单位的结果仅受其自己的治疗分配影响。干扰和相关的溢出可能是令人讨厌的，或者他们可能对研究人员有实质性的兴趣。在本章中，我们关注随机实验背景下的干扰。我们回顾了在一般网络设置中何时发生干扰的方法。然后我们考虑在层次结构中包含干扰的特殊情况。最后，我们讨论了干扰和传染之间的关系。我们使用干扰R包和模拟数据来说明关键点。我们考虑允许估计处理和溢出效应的有效设计，并讨论最近试图捕捉这种影响的实证研究。