A collection of papers related to LiNGAM

Please let me know if you find some other related papers :)

Standard LiNGAM

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: 2003--2030, 2006.
[pdf] [erratum] [Matlab/Octave code] [Google scholar]

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), Montreal, Canada, 2009.
[pdf] [notes] [Google scholar]

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. In Proc. 24th Conf. on Uncertainty in Artificial Intelligence (UAI2008), pp. 282--289, Helsinki, Finland, 2008.
[pdf] [Google scholar]

Bayesian approach

NEW R. Henao and O. Winther. Bayesian sparse factor models and DAGs inference and comparison. In Advances in Neural Information Processing Systems 22 (NIPS2009), pp. ?-?, 2010.
[pdf] [Google scholar]

P. O. Hoyer and A. Hyttinen. Bayesian discovery of linear acyclic causal models. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), Montreal, Canada, 2009.
[pdf] [code] [Google scholar]

Time series

J. Peters, D. Janzing, A. Gretton and B. Schölkopf. Detecting the direction of causal time series. In Proc. 26th International Conference on Machine Learning (ICML2009), Montreal, Canada, 2009.
[pdf] [Google scholar]

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.
[pdf] [videolecture] [Google scholar]

Latent variables

D. Janzing, J. Peters, J. Mooij and B. Schölkopf. Identifying confounders using additive noise models. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), Montreal, Canada, 2009.
[pdf] [Google scholar]

S. Shimizu, P. O. Hoyer and A. Hyvärinen. Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing, 72: 2024-2027, 2009.
[pdf] [preprint] [Google scholar]

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.
[pdf] [preprint (7.0MB)] [Matlab code] [Google scholar]

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.
[pdf] [preprint] [Google scholar]

Cyclic models

G. Lacerda, P. Spirtes, J. Ramsey and P. O. Hoyer. Discovering cyclic causal models by independent components analysis. In Proc. 24th Conf. on Uncertainty in Artificial Intelligence (UAI2008), Helsinki, Finland, 2008.
[pdf] [videolecture] [code] [Google scholar]

Pruning

A. B. Nielsen and L. K. Hansen. Structure learning by pruning in independent component analysis. Neurocomputing, 71: 2281-2290, 2008.
[pdf] [Google scholar]

K. Zhang and L. Chan. ICA with sparse connections. In Proc. 7th Conf. on Intelligent Data Engineering and Automated Learning (IDEAL2006), Burgos, Spain, 2006.
[pdf] [preprint?] [Google scholar]

Nonlinear models

NEW R. E. Tillman, A. Gretton, and P. Spirtes. Nonlinear directed acyclic structure learning with weakly additive noise models. In Advances in Neural Information Processing Systems 22 (NIPS2009), pp. ?-?, 2010.
[pdf] [Google scholar]

P. O. Hoyer, D. Janzing, J. Mooij, J. Peters and B. Schölkopf. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 21 (NIPS2008), pp. 689-696, 2009.
[pdf] [Google scholar]

J. Mooij, D. Janzing, J. Peters and B. Schölkopf. Regression by dependence minimization and its application to causal inference in additive noise models. In Proc. 26th International Conference on Machine Learning (ICML2009), Montreal, Canada, 2009.
[pdf] [Google scholar]

K. Zhang and A. Hyvärinen Causality discovery with additive disturbances: an information-theoretical perspective. In Proc. European Conference on Machine Learning (ECML2009), Bled, Slovenia, 2009.
[pdf] [preprint] [Google scholar]

K. Zhang and A. Hyvärinen. On the identifiability of the post-nonlinear causal model. In Proc. 25th Conf. on Uncertainty in Artificial Intelligence (UAI2009), Montreal, Canada, 2009.
[pdf] [Google scholar]

K. Zhang and A. Hyvärinen. Distinguishing causes from effects using nonlinear acyclic causal models. In JMLR: Workshop and Conference Proceedings (Proc. NIPS2008 workshop on causality), 2009.
[preprint] [videolecture] [Google scholar]

K. Zhang and L. Chan. Minimal nonlinear distortion principle for nonlinear independent component analysis. Journal of Machine Learning Research, 9: 2455--2487, 2008.
[pdf] [Google scholar]

Updated on 14th December 2009.
Updated on 18th November 2009.
Updated on 31st August 2009.
Updated on 20th June 2009.
Updated on 18th June 2009.
Updated on 25th May 2009.
Updated on 14th May 2009.
Created by Shohei Shimizu on 7th April 2009.