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

• URL: http://arxiv.org/abs/2502.20115v3
• Date: Fri, 26 Sep 2025 15:48:36 GMT
• Title: Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
• Authors: Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyvärinen,
• Abstract summary: Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity.<n>Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions.<n>We propose several multi-view causal discovery algorithms, inspired by single-view algorithms.
• Score: 59.15672758767244
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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