论文标题
网络数据中的自适应随机化
Adaptive Randomization in Network Data
论文作者
论文摘要
网络数据在最近的研究中经常出现。例如，在比较不同类型的治疗的影响时，已经提出了网络模型来提高估计和假设检验的质量。在本文中，我们专注于使用网络中的自适应随机程序有效估计平均治疗效果。我们研究因果框架的模型，为此，受试者的治疗结果受其自身协变量以及其邻居的影响。此外，我们考虑了当我们将治疗分配给当前主题时，仅揭示了现有主题的子网。提出了新的随机程序，以最大程度地减少治疗效果之间估计差异的平方误差。在网络数据中，通常很难获得理论属性，因为节点和连接的数量同时增加。在温和的假设下，我们提出的程序与随时间变化的不均匀马尔可夫链密切相关。然后，我们使用Lyapunov函数来得出所提出程序的理论特性。在实际网络数据上进行了广泛的模拟和实验，还证明了所提出程序的优势。
Network data have appeared frequently in recent research. For example, in comparing the effects of different types of treatment, network models have been proposed to improve the quality of estimation and hypothesis testing. In this paper, we focus on efficiently estimating the average treatment effect using an adaptive randomization procedure in networks. We work on models of causal frameworks, for which the treatment outcome of a subject is affected by its own covariate as well as those of its neighbors. Moreover, we consider the case in which, when we assign treatments to the current subject, only the subnetwork of existing subjects is revealed. New randomized procedures are proposed to minimize the mean squared error of the estimated differences between treatment effects. In network data, it is usually difficult to obtain theoretical properties because the numbers of nodes and connections increase simultaneously. Under mild assumptions, our proposed procedure is closely related to a time-varying inhomogeneous Markov chain. We then use Lyapunov functions to derive the theoretical properties of the proposed procedures. The advantages of the proposed procedures are also demonstrated by extensive simulations and experiments on real network data.
