Design and Analysis of Experiments in Networks: Reducing Bias from Interference
Design and Analysis of Experiments in Networks: Reducing Bias from Interference
复制标题
DOI：
10.1515/jci-2015-0021
复制
发表时间：
2017-03-01
影响因子：
1.4
通讯作者：
Ugander, Johan
中科院分区：
文献类型：
--
作者：
Eckles, Dean;Karrer, Brian;Ugander, Johan
由于干扰，估计网络中干预措施的效果是复杂的，因此一个实验单元的结果可能取决于其他单元的治疗分配。熟悉的统计形式主义，实验设计和分析方法假设没有这种干扰，并导致有偏见的因果关系的估计，当它存在。虽然一些假设可以导致无偏估计，但这些假设在网络的上下文中通常是不现实的，并且通常相当于假设干扰。在这项工作中，我们评估的方法设计和分析随机实验的最小，现实的假设兼容广泛的干扰，其目的是减少偏见和可能的整体误差估计的平均效果的全球治疗。在设计中，我们考虑对网络中相关的治疗进行随机分配的能力，例如通过图簇随机化。在分析中，我们考虑将有关网络邻居的治疗分配的信息。我们通过设计和分析证明了在存在潜在全局干扰的情况下偏倚降低的充分条件;这些条件也给出了治疗效果的下限。通过对网络实验的整个过程进行模拟，我们测量了这些方法在不同网络结构和不同社会行为下的性能，发现大量的偏差减少，尽管存在偏差-方差权衡，但错误减少。这些改进对于具有更多聚类和数据生成过程的网络是最大的，这些网络具有更强的处理直接效应和更强的单元之间的相互作用。
Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence of this interference, and result in biased estimates of causal effects when it exists. While some assumptions can lead to unbiased estimates, these assumptions are generally unrealistic in the context of a network and often amount to assuming away the interference. In this work, we evaluate methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, where the aim is to reduce bias and possibly overall error in estimates of average effects of a global treatment. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference; these conditions also give lower bounds on treatment effects. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias reductions and, despite a bias-variance tradeoff, error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.