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New multi-view causal discovery algorithms relax non-Gaussianity assumption

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for multi-view causal discovery, a method that aims to identify causal relationships within data by leveraging multiple related datasets. This approach relaxes the common requirement of non-Gaussian data, instead utilizing correlations across different views of the same system. The proposed multi-view linear Structural Equation Model extends existing frameworks and has been validated through simulations and applications in neuroimaging, enabling the estimation of causal graphs between brain regions. AI

IMPACT Advances causal inference techniques, potentially improving AI's ability to understand and model complex systems.

RANK_REASON Academic paper published on arXiv detailing new algorithms for causal discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-view causal discovery algorithms relax non-Gaussianity assumption

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Academic paper published on arXiv detailing new algorithms for causal discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyv\"arinen ·

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

    arXiv:2502.20115v4 Announce Type: replace-cross 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 sy…

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