Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms

Abstract:Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions. We propose a multi-view linear Structural Equation Model (SEM) that extends the well-known framework of non-Gaussian disturbances by alternatively leveraging correlation over views. We prove the identifiability of the model for acyclic SEMs. Subsequently, we propose several multi-view causal discovery algorithms, inspired by single-view algorithms (DirectLiNGAM, PairwiseLiNGAM, and ICA-LiNGAM). The new methods are validated through simulations and applications on neuroimaging data, where they enable the estimation of causal graphs between brain regions.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2502.20115 [cs.LG]

(or arXiv:2502.20115v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2502.20115 Focus to learn more arXiv-issued DOI via DataCite

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