标题： 模型、方法与网络拓扑：干扰研究的实验设计 显示英文标题

标题： Models, Methods and Network Topology: Experimental Design for the Study of Interference

摘要： 如何设计网络实验才能实现较高的统计功效？ 网络上的实验性处理可能会传播。 将治疗随机分配给节点可以增强对社会网络实验反事实因果效应的了解，同时也需要新的方法论（例如，Aronow 和 Samii，2017a；Bowers 等人，2013；Toulis 和 Kao，2013）。 本文显示了治疗在社会网络中的传播方式影响实验设计的统计功效。 因此，在实验设计中应考虑有关治疗传播的先验信息。 我们的发现证明了在简单实验中即使单元被认为独立时的标准做法也应重新考虑：当我们将一半单元分配到处理组，另一半分配到对照组时，并未最大化治疗效果的信息。 我们还提供了一个例子，说明统计功效取决于节点的网络度与治疗分配概率的相关程度。 我们建议研究人员在设计网络实验时仔细考虑其研究背后的治疗传播模型。 显示英文摘要

摘要： How should a network experiment be designed to achieve high statistical power? Ex- perimental treatments on networks may spread. Randomizing assignment of treatment to nodes enhances learning about the counterfactual causal effects of a social network experiment and also requires new methodology (ex. Aronow and Samii 2017a; Bow- ers et al. 2013; Toulis and Kao 2013). In this paper we show that the way in which a treatment propagates across a social network affects the statistical power of an ex- perimental design. As such, prior information regarding treatment propagation should be incorporated into the experimental design. Our findings justify reconsideration of standard practice in circumstances where units are presumed to be independent even in simple experiments: information about treatment effects is not maximized when we assign half the units to treatment and half to control. We also present an exam- ple in which statistical power depends on the extent to which the network degree of nodes is correlated with treatment assignment probability. We recommend that re- searchers think carefully about the underlying treatment propagation model motivat- ing their study in designing an experiment on a network.