Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
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Computer Science > Machine Learning
Title:Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
Abstract:Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG. Recovering the sparsest DAG from finite samples, however, remains difficult. Although existing methods are asymptotically consistent, they do not provide an explicit finite-sample procedure for recovering the unique sparsest DAG, nor do they handle models with an arbitrary number of latent confounders.
In this paper, we propose a finite-sample method for recovering the sparsest DAG without imposing any restriction on the number of latent confounders. Simulation studies and real-data analyses demonstrate that the proposed method achieves superior finite-sample performance compared with existing approaches.
| Comments: | 23 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.05984 [cs.LG] |
| (or arXiv:2607.05984v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05984
arXiv-issued DOI via DataCite (pending registration)
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