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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.14780 |
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| _version_ | 1866918161923702784 |
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| author | Cai, Ming Gao, Penggang Hara, Hisayuki |
| author_facet | Cai, Ming Gao, Penggang Hara, Hisayuki |
| contents | This paper addresses the problem of estimating causal directed acyclic graphs in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM). Existing methods assume mutually independent latent confounders or cannot properly handle models with causal relationships among observed variables.
We propose a novel algorithm that identifies causal DAGs in LvLiNGAM, allowing causal structures among latent variables, among observed variables, and between the two. The proposed method leverages higher-order cumulants of observed data to identify the causal structure. Extensive simulations and experiments with real-world data demonstrate the validity and practical utility of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants Cai, Ming Gao, Penggang Hara, Hisayuki Machine Learning This paper addresses the problem of estimating causal directed acyclic graphs in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM). Existing methods assume mutually independent latent confounders or cannot properly handle models with causal relationships among observed variables. We propose a novel algorithm that identifies causal DAGs in LvLiNGAM, allowing causal structures among latent variables, among observed variables, and between the two. The proposed method leverages higher-order cumulants of observed data to identify the causal structure. Extensive simulations and experiments with real-world data demonstrate the validity and practical utility of the proposed algorithm. |
| title | Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.14780 |