What do randomized studies of housing mobility demonstrate?: Causal inference in the face of interference
What do randomized studies of housing mobility demonstrate?: Causal inference in the face of interference
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DOI：
10.1198/016214506000000636
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发表时间：
2006-12-01
影响因子：
3.7
通讯作者：
Sobel, Michael E.
中科院分区：
文献类型：
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作者：
Sobel, Michael E.
在过去的20年里，社会科学家使用观察研究产生了大量关于邻里效应的非决定性文献。最近的工作者认为，基于住房流动性的随机研究，如“向机会移动”（MTO）示范，对邻里效应的估计更可信。这些估计值基于单位之间无干扰的隐含假设;即，受试者对应答的值仅取决于该受试者分配的治疗，而不取决于其他受试者的治疗分配。对于MTO研究，这种假设是不合理的。虽然在存在干扰时对治疗效果的定义和估计方面做的工作很少，但在邻里效应和许多其他社会环境（例如，学校和网络），当从这些研究中获得的数据在“无干扰假设”下进行分析时，可能会产生非常误导的推论。此外，干扰的后果（例如，溢出效应）往往具有重大的实质性意义，尽管人们对此关注甚少。使用MTO示范作为一个具体的背景下，本文开发了一个框架，因果推理时，干扰存在，并定义了一些因果被估量的利益。治疗效果，这是无偏的和/或一致的随机化研究中，没有干扰的通常估计的属性，也被表征。当存在干扰时，治疗组平均值和对照组平均值（未校正或校正协变量）之间的差异估计的不是平均治疗效应，而是两个不同亚群中定义的两种效应之间的差异。这一结果非常重要，因为没有认识到这一点的研究人员很容易推断出某种治疗方法是有益的，而事实上它是普遍有害的。
During the past 20 years, social scientists using observational studies have generated a large and inconclusive literature on neighborhood effects. Recent workers have argued that estimates of neighborhood effects based on randomized studies of housing mobility, such as the "Moving to Opportunity" (MTO) demonstration, are more credible. These estimates are based on the implicit assumption of no interference between units; that is, a subject's value on the response depends only on the treatment to which that subject is assigned, not on the treatment assignments of other subjects. For the MTO studies, this assumption is not reasonable. Although little work has been done on the definition and estimation of treatment effects when interference is present, interference is common in studies of neighborhood effects and in many other social settings (e.g., schools and networks), and when data from such studies are analyzed under the "no-interference assumption," very misleading inferences can result. Furthermore, the consequences of interference (e.g., spillovers) should often be of great substantive interest, even though little attention has been paid to this. Using the MTO demonstration as a concrete context, this article develops a framework for causal inference when interference is present and defines a number of causal estimands of interest. The properties of the usual estimators of treatment effects, which are unbiased and/or consistent in randomized studies without interference, are also characterized. When interference is present, the difference between a treatment group mean and a control group mean (unadjusted or adjusted for covariates) estimates not an average treatment effect, but rather the difference between two effects defined on two distinct subpopulations. This result is of great importance, for a researcher who fails to recognize this could easily infer that a treatment is beneficial when in fact it is universally harmful.