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A linear non-Gaussian acyclic model for causal discovery
S Shimizu, PO Hoyer, A Hyvärinen, A Kerminen
Journal of Machine Learning Research 7, 2003-2030, 2006
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
S Shimizu, T Inazumi, Y Sogawa, A Hyvarinen, Y Kawahara, T Washio, ...
Journal of Machine Learning Research 12, 1225-1248, 2011
Estimation of a structural vector autoregression model using non-Gaussianity
A Hyvärinen, K Zhang, S Shimizu, PO Hoyer
Journal of Machine Learning Research 11, 1709-1731, 2010
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
PO Hoyer, S Shimizu, AJ Kerminen, M Palviainen
International Journal of Approximate Reasoning 49 (2), 362-378, 2008
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
A Hyvärinen, S Shimizu, PO Hoyer
The 25th International Conference on Machine learning (ICML2008), 424-431, 2008
Causal discovery of linear acyclic models with arbitrary distributions
PO Hoyer, A Hyvärinen, R Scheines, P Spirtes, J Ramsey, G Lacerda, ...
The 24th Conference on Uncertainty in Artificial Intelligence (UAI2008), 2008
LiNGAM: Non-Gaussian methods for estimating causal structures
S Shimizu
Behaviormetrika 41 (1), 65-98, 2014
Use of non-normality in structural equation modeling: Application to direction of causation
S Shimizu, Y Kano
Journal of Statistical Planning and Inference 138 (11), 3483-3491, 2008
Causal inference using nonnormality
Y Kano, S Shimizu
International Symposium on Science of Modeling, the 30th Anniversary of the …, 2003
Discovery of non-gaussian linear causal models using ICA
S Shimizu, A Hyvarinen, Y Kano, PO Hoyer
The 21st Conference on Uncertainty in Artificial Intelligence (UAI2005), 2005
Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions
S Shimizu, K Bollen
Journal of Machine Learning Research 15, 2629-2652, 2014
ParceLiNGAM: A causal ordering method robust against latent confounders
T Tashiro, S Shimizu, A Hyvarinen, T Washio
Neural Computation 26 (1), 57-83, 2014
Multi-modality behavioral influence analysis for personalized recommendations in health social media environment
X Zhou, W Liang, I Kevin, K Wang, S Shimizu
IEEE Transactions on Computational Social Systems 6 (5), 888-897, 2019
Cause-effect inference by comparing regression errors
P Blöbaum, D Janzing, T Washio, S Shimizu, B Schölkopf
International Conference on Artificial Intelligence and Statistics …, 2018
Finding a causal ordering via independent component analysis
S Shimizu, A Hyvärinen, PO Hoyer, Y Kano
Computational Statistics & Data Analysis 50 (11), 3278-3293, 2006
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
S Shimizu, A Hyvärinen, Y Kawahara, T Washio
The 25th Conference on Uncertainty in Artificial Intelligence (UAI2009), 506-513, 2009
Estimation of linear non-Gaussian acyclic models for latent factors
S Shimizu, PO Hoyer, A Hyvärinen
Neurocomputing 72 (7-9), 2024-2027, 2009
Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems
X Zhou, W Liang, S Shimizu, J Ma, Q Jin
IEEE Transactions on Industrial Informatics 17 (8), 5790-5798, 2020
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
PO Hoyer, S Shimizu, AJ Kerminen
The 3rd European Workshop on Probabilistic Graphical Models (PGM2006), 2006
Joint estimation of linear non-Gaussian acyclic models
S Shimizu
Neurocomputing 81, 104-107, 2012
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