Reasoning about Interference Between Units: A General Framework
Reasoning about Interference Between Units: A General Framework
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DOI：
10.1093/pan/mps038
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发表时间：
2013-12-01
影响因子：
5.4
通讯作者：
Panagopoulos, Costas
中科院分区：
文献类型：
--
作者：
Bowers, Jake;Fredrickson, Mark M.;Panagopoulos, Costas
如果一个实验性的治疗是由治疗组和对照组的单位，测试的因果关系的假设可能很难概念化，更不用说执行。在这篇文章中，我们将展示如何反事实因果模型可能会被写入和测试时，理论表明溢出或其他基于网络的实验单位之间的干扰。我们表明，“无干扰”的假设不需要约束学者谁有有趣的问题干扰。我们为研究人员提供了建模理论的能力，这些理论是关于某些单位的治疗如何影响其他单位的结果的。我们进一步展示了如何测试这些因果效应的假设，我们提供的工具，使研究人员能够评估他们的测试给自己的模型，设计，测试统计和数据的操作特性。我们在这里开发的概念和方法框架特别适用于社交网络，但每当研究人员想知道单元之间的干扰时，可以有效地部署。单元之间的干扰不一定是一个不可检验的假设;相反，干扰是一个机会，可以对理论上有趣的现象提出有意义的问题。
If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize, let alone execute. In this article, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the "no interference" assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.