Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between. Interestingly, we notice that this structure can be identified through the utilization of higher-order cumulants. By leveraging the higher-order cumulants of non-Gaussian data, we provide an analytical solution for estimating the causal coefficients or their ratios. With the estimated (ratios of) causal coefficients, we propose a novel approach to identify the existence of a causal edge between two observed variables subject to latent variable influence. In case when such a causal edge exits, we introduce an asymmetry criterion to determine the causal direction. The experimental results demonstrate the effectiveness of our proposed method.

通过利用非高斯数据的高阶累积量，我们提出一种用于估计因果系数或其比例的解析解，并通过估计后的（比例）因果系数，提出一种新的方法来确定受潜变量影响的两个观测变量之间是否存在因果边，以及在存在这样的因果边时引入了一种非对称性准则来确定因果方向，实验结果表明了我们提出方法的有效性。

基于高阶累积量的潜变量因果结构识别