arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.05444stat.APcs.SIstat.ME
收藏

实验数据中的溢出效应

Spillover Effects in Experimental Data

Peter M. Aronow, Dean Eckles, Cyrus Samii, Stephanie Zonszein

摘要:

我们目前的方法估计处理效应和溢出效应下的“干扰”,一个术语,它涵盖了广泛的一类情况下,一个单位的结果不仅取决于该单位收到的治疗,但也对其他单位收到的治疗。在某种程度上,单位相互反应,相互作用,或以其他方式传递治疗效果,有效的推理要求我们考虑到这种干扰,这是一个背离传统的假设,即单位的结果只受自己的治疗分配。干扰和相关的溢出效应可能是一种滋扰,也可能是研究人员的实质性兴趣。在本章中,我们将重点讨论随机实验背景下的干扰。我们审查的方法,当干扰发生在一般的网络设置。然后,我们考虑的特殊情况下,干扰包含在一个层次结构。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。我们认为有效的设计,允许估计的治疗和溢出效应,并讨论最近的实证研究,试图捕捉这种影响。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑