Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Stat Methods Med Res. 2012 Feb;21(1):55-75. doi: 10.1177/0962280210386779. Epub 2010 Nov 10.
Interference is said to be present when the exposure or treatment received by one individual may affect the outcomes of other individuals. Such interference can arise in settings in which the outcomes of the various individuals come about through social interactions. When interference is present, causal inference is rendered considerably more complex, and the literature on causal inference in the presence of interference has just recently begun to develop. In this article we summarise some of the concepts and results from the existing literature and extend that literature in considering new results for finite sample inference, new inverse probability weighting estimators in the presence of interference and new causal estimands of interest.
当一个人所接受的暴露或治疗可能影响到其他人的结果时，就会出现干扰。这种干扰可能出现在各种个体的结果是通过社会互动产生的环境中。当存在干扰时，因果推断就变得更加复杂，而关于存在干扰时的因果推断的文献刚刚开始发展。在本文中，我们总结了现有文献中的一些概念和结果，并在考虑有限样本推断的新结果、干扰存在时的新逆概率加权估计量以及新的感兴趣的因果估计量时扩展了该文献。
