Publications by topic |
Bayesian networks, causal analysis, structural equation models[Here we propose a new identifiable framework for estimation/learning of continuous-valued Bayesian networks or structural equation models, with application in causal analysis. The framework has linear and non-linear variants.]Linear models
S. Shimizu, P.O. Hoyer, A. Hyvärinen, and A. Kerminen.
A Linear Non-Gaussian Acyclic Model for Causal Discovery.
J. of Machine Learning Research 7:2003-2030, 2006.
R. Pio Monti and A. Hyvärinen.
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data.
UAI2018.
A. Hyvärinen and S. M. Smith.
Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models.
J. of Machine Learning Research 14:111-152, 2013.
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. J. of Machine Learning Research 12:1225-1248, 2011.
Y. Sogawa, S. Shimizu, A. Hyvärinen, T. Washio, T. Shimamura and S. Imoto.
Estimating Exogenous Variables in Data with More Variables than Observations.. Neural Networks, 24:875--880, 2011.
A. Hyvärinen, K. Zhang, S. Shimizu, and P.O. Hoyer.
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
J. of Machine Learning Research, 11:1709-1731, 2010.
P.O. Hoyer, A. Hyvärinen, R. Scheines, P. Spirtes, J. Ramsey, G. Lacerda, and S. Shimizu.
Causal discovery of linear acyclic models with arbitrary distributions.
Conf. on Uncertainty in Artificial Intelligence (UAI2008), Helsinki, Finland.
S. Shimizu, P.O. Hoyer, and A. Hyvärinen
Estimation of linear non-Gaussian acyclic models for latent factors.
Neurocomputing, 72:2024-2027, 2009.
S. Shimizu, A. Hyvärinen
Discovery of linear non-gaussian acyclic models in the presence of latent classes.
Proc. Int. Conf. on Neural Information Processing (ICONIP2007), pp. 752--761, 2008.
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.
Non-linear models
R. Pio Monti, K. Zhang, and A. Hyvärinen.
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA.
UAI2019.
K. Zhang and A. Hyvärinen.
Nonlinear Functional Causal Models for Distinguishing Cause from Effect. In Statistical and Causality.
K. Zhang and A. Hyvärinen.
On the Identifiability of the Post-Nonlinear Causal Model. UAI 2009.
K. Zhang and A. Hyvärinen.
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML 2009.
K. Zhang and A. Hyvärinen.
Distinguishing Causes from Effect using Nonlinear Acyclic Causal Models.
JMLR Workshop and Conference Proceedings. Causality: Objectives and Assessment (NIPS 2008), 6:157-164, 2010.
J. Hirayama and A. Hyvärinen.
Structural equations and divisive normalization for energy-dependent component analysis.
Advances in Neural Information Processing (NIPS2011), Granada, Spain, 2012.
K. Zhang and A. Hyvärinen.
Source separation and higher-order causal analysis of MEG and EEG.
UAI2010. |