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.
Cite
@article{arxiv.2502.20115,
title = {Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms},
author = {Ambroise Heurtebise and Omar Chehab and Pierre Ablin and Alexandre Gramfort and Aapo Hyvärinen},
journal= {arXiv preprint arXiv:2502.20115},
year = {2025}
}