ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders

DOI10.1162/NECO_a_00533zbMath1410.68325arXiv1303.7410OpenAlexW2135404705WikidataQ48952309 ScholiaQ48952309MaRDI QIDQ5378311

Aapo Hyvärinen, Takashi Washio, Shohei Shimizu, Tatsuya Tashiro

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.7410

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Causal discovery in heavy-tailed models ⋮ Unnamed Item ⋮ Estimating bounds on causal effects in high-dimensional and possibly confounded systems ⋮ A causal discovery algorithm based on the prior selection of leaf nodes ⋮ Identifiability of latent-variable and structural-equation models: from linear to nonlinear ⋮ Multi-Trek Separation in Linear Structural Equation Models ⋮ Unnamed Item

Uses Software

• TETRAD
• DirectLiNGAM

Cites Work

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• Causal diagrams for empirical research
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• Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire