Aapo Hyvärinen: Publications

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Brain imaging data analysis Bayesian networks & SEM Estimation theory Natural image statistics The FastICA algorithm Independent component analysis: misc. Unsupervised learning: misc. Experimental vision research   Publications front page Back to home page

Bayesian networks, causal analysis, structural equation models

[Here we propose a new framework, based on non-Gaussianity, 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.
pdf    matlab/octave code
[Proposes a new framework, "LiNGAM", for structural equation modelling, continuous-valued Bayesian networks, and causal inference based on non-gaussianity. Based on UAI2005 paper.]

A. Hyvärinen. Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models JMLR Conference and Workshop Proceedings vol. 13 (ACML2010), pp. 1-16 2010.
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[Proposes new estimators for the LiNGAM model of the preceding paper. These estimators have simple intuitive interpretations and are statistically better at least in some circumstances.]

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.
pdf matlab code
[A new algorithm for estimating the model proposed in the JMLR2005 paper. Based on our UAI 2009 paper.]

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.
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[Considers causal discovery using non-gaussianity in the case where the number of variables is very large.]

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.
pdf   Videolecture
[Shows how to combine classic autoregressive modelling with instantaneous Bayesian networks (LiNGAM). Based on ICML2008 and ECML2009 papers.]

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.
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[Extends the LiNGAM approach to the case where some of the variables can be gaussian.]

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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[Extends the LiNGAM framework to causal connections between latent factors.]

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.
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[Extends the LiNGAM framework to the case of latent classes.]

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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[Shows how a causal ordering can be estimated for non-gaussian variables based on ICA. An earlier version of the LiNGAM framework.]

Non-linear models

K. Zhang and A. Hyvärinen. Source separation and higher-order causal analysis of MEG and EEG UAI2010.
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[Develops a model of causal relations of variances of time signals.]

K. Zhang and A. Hyvärinen. On the Identifiability of the Post-Nonlinear Causal Model.UAI 2009.
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[Analyzes further the identifiability of the nonlinear model of the preceding paper. Related to the identifiability on post-nonlinear ICA, for which we obtain somewhat surprising results.]

K. Zhang and A. Hyvärinen. Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML 2009.
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[Provides a general theoretical framework for the LiNGAM model, and applies it on nonlinear and time-dependent models.]

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.
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[Develops a strongly nonlinear version of the LiNGAM framework introduced above. See UAI2009 paper for more recent results.]