论文标题
检测在k-nearest-neighbors干扰模型下的治疗干扰
Detecting Treatment Interference under the K-Nearest-Neighbors Interference Model
论文作者
论文摘要
我们提出了一种治疗干扰的模型，其中一个单位的响应仅取决于其治疗状态和K-Neighborhood中单位的状态。当前用于检测干扰的方法包括一组焦点单元上精心设计的随机实验和条件随机测试。我们为如何在这种干扰模型下选择焦点单位提供指导。然后，我们进行了一项模拟研究，以评估现有方法检测网络干扰的功效。我们表明，这种焦点单元的选择会导致对治疗干扰的强大测试，从而超过了当前的实验方法。
We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units under this model of interference. We then conduct a simulation study to evaluate the efficacy of existing methods for detecting network interference. We show that this choice of focal units leads to powerful tests of treatment interference which outperform current experimental methods.
