论文标题
最小化网络实验设计中的干扰和选择偏差
Minimizing Interference and Selection Bias in Network Experiment Design
论文作者
论文摘要
当前在网络中进行A/B测试的方法集中在限制干扰上，关注治疗效果可能会从治疗节点“溢出”到控制淋巴结并导致因果效应估计的偏见。网络实验设计的突出方法依赖于两阶段的随机化，其中识别出稀疏连接的簇并聚类随机分组决定了节点分配对治疗和控制。在这里，我们表明群集随机化不能确保足够的节点随机化，并且可以导致选择偏差，在这种偏差中，治疗和控制节点代表不同的用户群体。为了解决这个问题，我们提出了一个用于网络实验设计的原则性框架，该框架共同最大程度地减少了干扰和选择偏见。我们介绍了边缘溢出概率和群集匹配的概念，并证明了它们在设计网络A/B测试中的重要性。我们对许多现实世界数据集的实验表明，我们提出的框架导致因果效应估计的误差明显低于现有解决方案。
Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.
