Identifiability of Gaussian linear structural equation models with homogeneous and heterogeneous error variances

DOI10.1007/s42952-019-00019-7zbMath1485.62073arXiv1901.10134OpenAlexW2997194834MaRDI QIDQ2131903

Youngwhan Kim, Gunwoong Park

Publication date: 27 April 2022

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.10134

zbMATH Keywords

identifiabilityBayesian networkcausal inferencestructural equation modeldirected acyclic graphical model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (7)

Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances ⋮ Unnamed Item ⋮ Densely connected sub-Gaussian linear structural equation model learning via \(\ell_1\)- and \(\ell_2\)-regularized regressions ⋮ Scalable Bayesian high-dimensional local dependence learning ⋮ Unnamed Item ⋮ Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances ⋮ Robust estimation of Gaussian linear structural equation models with equal error variances

Uses Software

• TETRAD
• DirectLiNGAM
• MIM

Cites Work

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• CAM: causal additive models, high-dimensional order search and penalized regression
• The three faces of faithfulness
• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• Learning Bayesian networks: The combination of knowledge and statistical data
• High-dimensional learning of linear causal networks via inverse covariance estimation
• Introduction to Graphical Modelling
• 10.1162/153244303321897717
• Identifiability of Gaussian structural equation models with equal error variances

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