模型、方法与网络拓扑：干扰研究的试验设计
Models, Methods and Network Topology: Experimental Design for the Study of Interference
摘要：
如何设计网络实验以实现高统计功效？网络上的实验性治疗可能会传播开来。将治疗随机分配给节点可以增强对社会网络实验的反事实因果效应的学习，也需要新的方法（例如，Aronow和Samii 2017 a; Bowers等人2013; Toulis和Kao 2013）。在本文中，我们表明，治疗传播的方式在社会网络影响的统计功效的实验设计。因此，有关治疗传播的先验信息应纳入试验设计。我们的研究结果证明，在单位被假定为独立的情况下，即使在简单的实验中，重新考虑标准的做法：当我们分配一半的单位治疗和一半的控制时，有关处理效应的信息并没有最大化。我们还提出了一个例子，其中统计功效取决于节点的网络度与治疗分配概率相关的程度。我们建议研究者在设计网络实验时仔细考虑潜在的治疗传播模型。
英文摘要：
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.
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