Publications by topic 
Causal discovery, Bayesian networks, SEM[Here we propose new identifiable frameworks for estimation/learning of structural equation models, with applications in causal discovery. The framework has linear and nonlinear variants.]Nonlinear models
H. Morioka and A. Hyvärinen.
Connectivitycontrastive learning: Combining causal discovery and representation learning for multimodal data. AISTATS2023.
I. Khemakhem, R. Pio Monti, R. Leech, and A. Hyvärinen.
Causal Autoregressive Flows. AISTATS2021.
R. Pio Monti, K. Zhang, and A. Hyvärinen.
Causal Discovery with General NonLinear Relationships Using NonLinear 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 PostNonlinear Causal Model. UAI 2009.
K. Zhang and A. Hyvärinen.
Causality Discovery with Additive Disturbances: An InformationTheoretical 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), 6:157164, 2010.
J. Hirayama and A. Hyvärinen.
Structural equations and divisive normalization for energydependent component analysis.
Advances in Neural Information Processing (NIPS2011), Granada, Spain, 2012.
K. Zhang and A. Hyvärinen.
Source separation and higherorder causal analysis of MEG and EEG.
UAI2010. Linear models
S. Shimizu, P.O. Hoyer, A. Hyvärinen, and A. Kerminen.
A Linear NonGaussian Acyclic Model for Causal Discovery.
J. of Machine Learning Research 7:20032030, 2006.
R. Pio Monti and A. Hyvärinen.
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from HighDimensional Data.
UAI2018.
A. Hyvärinen and S. M. Smith.
Pairwise Likelihood Ratios for Estimation of NonGaussian Structural Equation Models.
J. of Machine Learning Research 14:111152, 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 nonGaussian structural equation model. J. of Machine Learning Research 12:12251248, 2011.
A. Hyvärinen, K. Zhang, S. Shimizu, and P.O. Hoyer.
Estimation of a Structural Vector Autoregression Model Using NonGaussianity
J. of Machine Learning Research, 11:17091731, 2010.
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, 24:875880, 2011.
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 nonGaussian acyclic models for latent factors.
Neurocomputing, 72:20242027, 2009.
S. Shimizu, A. Hyvärinen
Discovery of linear nongaussian acyclic models in the presence of latent classes.
Proc. Int. Conf. on Neural Information Processing (ICONIP2007), pp. 752761, 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):32783293, 2006.
