Identifiable Multi-View Causal Discovery Without Non-Gaussianity

• URL: http://arxiv.org/abs/2502.20115v2
• Date: Fri, 28 Feb 2025 17:33:29 GMT
• Title: Identifiable Multi-View Causal Discovery Without Non-Gaussianity
• Authors: Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyvärinen,
• Abstract summary: We propose a novel approach to linear causal discovery in the framework of multi-view Structural Equation Models (SEM)<n>We prove the identifiability of all the parameters of the model without any further assumptions on the structure of the SEM other than it being acyclic.<n>The proposed methodology is validated through simulations and application on real data, where it enables the estimation of causal graphs between brain regions.
• Score: 63.217175519436125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel approach to linear causal discovery in the framework of multi-view Structural Equation Models (SEM). Our proposed model relaxes the well-known assumption of non-Gaussian disturbances by alternatively assuming diversity of variances over views, making it more broadly applicable. We prove the identifiability of all the parameters of the model without any further assumptions on the structure of the SEM other than it being acyclic. We further propose an estimation algorithm based on recent advances in multi-view Independent Component Analysis (ICA). The proposed methodology is validated through simulations and application on real neuroimaging data, where it enables the estimation of causal graphs between brain regions.

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