Authors' versions

T. N. Maeda and S. Shimizu. Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. International Journal of Data Science and Analytics, xx: xx–xx, 2021.
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S. Shimizu and P. Blöbaum. Recent advances in semi-parametric methods for causal discovery. In Direction Dependence in Statistical Models: Methods of Analysis (W. Wiedermann, D. Kim, E. Sungur, and A. von Eye, eds.), pages xx–xx. Wiley, 2020.
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K. Uemura and S. Shimizu. Estimation of post-nonlinear causal models using autoencoding structure. In Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pp. xx--xx, Barcelona, Spain, 2020.
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S. Shimizu. Non-Gaussian methods for causal structure learning. Prevention Science, xx: xx--xx, 2018.
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R. Silva and S. Shimizu. Learning instrumental variables with structural and non-Gaussianity assumptions. Journal of Machine Learning Research, 18: 1--49, 2017.
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P. Blöbaum and S. Shimizu. Estimation of interventional effects of features on prediction. In Proc. 2017 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017), pp. xx--xx, Tokyo, Japan, 2017.
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S. Shimizu. A non-Gaussian approach for causal discovery in the presence of hidden common causes. In Proc. Second Workshop on Advanced Methodologies for Bayesian Networks (AMBN2015), pp. 222--233, Yokohama, Japan, 2015.
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R. Silva and S. Shimizu. Learning instrumental variables with non-Gaussianity assumptions: Theoretical limitations and practical algorithms. Arxiv preprint arXiv:1511.02722, 2015.
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N. Tanaka, S. Shimizu and T. Washio. A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes. Arxiv preprint arXiv:1408.0337, 2014.
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S. Shimizu. LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014.
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S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629--2652, 2014.
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K. Kadowaki, S. Shimizu and T. Washio. Estimation of causal structures in longitudinal data using non-Gaussianity. In Proc. 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013), pp. 1--6, Southampton, United Kingdom, 2013.
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K. Thamvitayakul, S. Shimizu, T. Ueno, T. Washio and T. Tashiro. Bootstrap confidence intervals in DirectLiNGAM. In Proc. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW2012), pp.659--668, Brussels, Belgium, 2012.
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T. Tashiro, S. Shimizu, A. Hyvärinen and T. Washio.
ParceLiNGAM: A causal ordering method robust against latent confounders.
Neural Computation, 26(1): 57--83, 2014.
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T. Tashiro, S. Shimizu, A. Hyvärinen and T. Washio. Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders. In Proc. 22nd International Conference on Artificial Neural Networks (ICANN2012), pp. 491--498, Lausanne, Switzerland, 2012.
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S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.
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J. Suzuki, T. Inazumi, T. Washio and S. Shimizu. Identifiability of an integer modular acyclic additive noise model and its causal structure discovery. Arxiv preprint arXiv:1401.5625, 2014.
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T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto and Y. Kawahara. Causal discovery in a binary exclusive-or skew acyclic model: BExSAM. Arxiv preprint arXiv:1401.5636, 2014.
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T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto and Y. Kawahara. Discovering causal structures in binary exclusive-or skew acyclic models. In Proc. 27th Conf. on Uncertainty in Artificial Intelligence (UAI2011), pp. 373--382, Barcelona, Spain, 2011.
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Y. Kawahara, K. Bollen, S. Shimizu, T. Washio. GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. Arxiv preprint arXiv:1006.5041, 2010.
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Y. Komatsu, S. Shimizu and H. Shimodaira. Assessing statistical reliability of LiNGAM via multiscale bootstrap. In Proc. International Conference on Artificial Neural Networks (ICANN2010), pp.309-314, Thessaloniki, Greece, 2010.
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S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(Apr): 1225--1248, 2011.
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T. Inazumi, S. Shimizu and T. Washio. Use of prior knowledge in a non-Gaussian method for learning linear structural equation models. In Proc. 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA2010), Saint-Malo, France, pp.221--228, 2010.
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Y. Sogawa, S. Shimizu, Y. Kawahara and T. Washio. An experimental comparison of linear non-Gaussian causal discovery methods and their variants. In Proc. Int. Joint Conference on Neural Networks (IJCNN2010), pp. 768--775, Barcelona, Spain, 2010.
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S. Shimizu, A. Hyvärinen, Y. Kawahara and T. Washio. A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), pp. 506-513, Montreal, Canada, 2009.

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Y. Sogawa, S. Shimizu, T. Shimamura, A. Hyvärinen, T. Washio and S. Imoto. Estimating exogenous variables in data with more variables than observations. Neural Networks, 24(8): 875-880, 2011 (Selected papers from ICANN2010).
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Y. Sogawa, S. Shimizu, A. Hyvärinen, T. Washio, T. Shimamura and S. Imoto. Discovery of exogenous variables in data with more variables than observations. In Proc. International Conference on Artificial Neural Networks (ICANN2010), pp.67-76, Thessaloniki, Greece, 2010.
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A. Hyvärinen, K. Zhang, S. Shimizu, P. O. Hoyer. Estimation of a structural vector autoregressive model using non-Gaussianity. Journal of Machine Learning Research, 11(May): 1709−1731, 2010.

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A. Hyvärinen, S. Shimizu and P. O. Hoyer. Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. In Proc. Int. Conf. on Machine Learning (ICML2008), pp. 424-431, Helsinki, Finland, 2008.
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P. O. Hoyer, A. Hyvärinen, R. Scheines, P. Spirtes, J. Ramsey, G. and S. Shimizu. Causal discovery of linear acyclic models with arbitrary distributions. In Proc. 24th Conf. on Uncertainty in Artificial Intelligence (UAI2008), pp. 282-289, Helsinki, Finland, 2008.
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S. Shimizu and A. Hyvärinen. Discovery of linear non-gaussian acyclic models in the presence of latent classes. In Proc. 14th Int. Conf. on Neural Information Processing (ICONIP2007), pp. 752-761, Kitakyushu, Japan, 2008.

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S. Shimizu, P. O. Hoyer and A. Hyvärinen. Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing, 72: 2024-2027, 2009.
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清水昌平. 独立成分分析による線形逐次モデルの探索. 日本統計学会誌, 37(2): 223−237, 2008.
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P. O. Hoyer, S. Shimizu, A. Kerminen and M. Palviainen. Estimation of causal effects using linear non-gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49(2): 362-378, 2008.
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P. O. Hoyer, S. Shimizu and A. Kerminen. Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. In Proc. the third European Workshop on Probabilistic Graphical Models (PGM2006), pp. 155--162, Prague, Czech Republic, 2006.
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A. Hyvärinen and S. Shimizu. A quasi-stochastic gradient algorithm for variance-dependent component analysis. In Proc. International Conference on Artificial Neural Networks (ICANN2006), pp. 211--220, Athens, Greece, 2006.
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S. Shimizu, P. O. Hoyer, A. Hyvärinen and A. Kerminen. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(Oct): 2003--2030, 2006.

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S. Shimizu, A. Hyvärinen, Y. Kano, P. O. Hoyer and A. Kerminen. Testing significance of mixing and demixing coefficients in ICA. In Proc. 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA2006), pp. 901--908, Charleston, SC, USA, 2006.
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P. O. Hoyer, S. Shimizu, A. Hyvärinen, Y. Kano and A. Kerminen. New permutation algorithms for causal discovery using ICA. In Proc. 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA2006), pp. 115--122, Charleston, SC, USA, 2006.
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S. Shimizu, A. Hyvärinen, Y. Kano and P. O. Hoyer. Discovery of non-gaussian linear causal models using ICA. In Proc. 21st Conf. on Uncertainty in Artificial Intelligence (UAI2005), pp. 525-533, Edinburgh, Scotland, 2005.
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S. Shimizu, A. Hyvärinen, P. O. Hoyer and Y. Kano. Finding a causal ordering via independent component analysis. Computational Statistics & Data Analysis, 50(11): 3278–3293, 2006.
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S. Shimizu, A. Hyvärinen and Y. Kano. A generalized least squares approach to blind separation of sources which have variance dependencies. In Proc. 13rd IEEE Workshop on Statistical Signal Processing (SSP05), pp. 1080--1083, Bordeaux, France, 2005.
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S. Shimizu and Y. Kano. Use of non-normality in structural equation modeling: Application to direction of causation. Journal of Statistical Planning and Inference, 138: 3483--3491, 2008.
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Y. Kano and S. Shimizu. Causal inference using nonnormality. In Proc. International Symposium on Science of Modeling-The 30th Anniversary of the Information Criterion (AIC)-, pp. 261--270, Tokyo, Japan, 2003.
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Y. Kano, Y. Miyamoto and S. Shimizu. Factor rotation and ICA. In Proc. 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), pp. 101--105, Nara, Japan, 2003.
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