Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

• URL: http://arxiv.org/abs/2312.11934v1
• Date: Tue, 19 Dec 2023 08:20:19 GMT
• Title: Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
• Authors: Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang
• Abstract summary: 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.
• Score: 31.85295338809117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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