scientific article; zbMATH DE number 7255106
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Publication:4969132
zbMath1498.68256MaRDI QIDQ4969132
Publication date: 5 October 2020
Full work available at URL: https://jmlr.csail.mit.edu/papers/v21/19-664.html
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directed acyclic graphidentifiabilityBayesian networkcausal inferencestructure learningstructural equation modeling
Estimation in multivariate analysis (62H12) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22) Causal inference from observational studies (62D20)
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Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances ⋮ Densely connected sub-Gaussian linear structural equation model learning via \(\ell_1\)- and \(\ell_2\)-regularized regressions ⋮ 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
Cites Work
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- Geometry of the faithfulness assumption in causal inference
- The three faces of faithfulness
- The max-min hill-climbing Bayesian network structure learning algorithm
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Identifiability of Gaussian linear structural equation models with homogeneous and heterogeneous error variances
- High-dimensional learning of linear causal networks via inverse covariance estimation
- 10.1162/153244303321897717
- On causal discovery with an equal-variance assumption
- High-dimensional causal discovery under non-Gaussianity
- Linear Model Selection by Cross-Validation
- Identifiability of Gaussian structural equation models with equal error variances
- Score-based causal learning in additive noise models
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