基于多组典型相关变量的因果关系发现算法

现有的因果关系发现算法主要基于单个观察变量本身之间的因果关系,无法适用于多组观察变量,为此提出了一种多组典型相关变量的因果关系发现算法。首先,引入多组典型相关变量建立多组典型相关变量的线性非高斯无环模型并提出对应的目标函数;然后,采用梯度上升的方法求解目标函数,构建多组典型相关变量的因果关系网络。模拟实验验证了该算法的有效性,并在移动基站数据上发现了一批有价值的多组无线网络性能指标间的因果关系。

Causal discovery algorithm based on multiset canonical correlation variables

Existing causal discovery algorithms are mainly based on the observed variables,and cannot be applied to the causal discovery among multiple sets of observed variables.Hence,this paper proposed a multiset canonical correlation variables based causal discovery algorithm.First,it introduced multiset canonical correlation variables to establish a linear non-Gaussian acyclic model for them,and proposed a corresponding objective function.Then,it used the gradient as cent method to solve the objective function,and constructed a causal network over multiset canonical correlation variables.Simulation experiments verify the correctness and effectiveness of the algorithm,and find a number of valuable sets of wireless network performance indicators on the mobile base station dataset.