Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders
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Aapo Hyvärinen | Takashi Washio | Shohei Shimizu | Tatsuya Tashiro | Shohei Shimizu | T. Washio | Aapo Hyvärinen | Tatsuya Tashiro
[1] G. Darmois,et al. Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaire , 1953 .
[2] Michael I. Jordan,et al. Kernel independent component analysis , 2003 .
[3] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[4] Aapo Hyvärinen,et al. A Linear Non-Gaussian Acyclic Model for Causal Discovery , 2006, J. Mach. Learn. Res..
[5] Aapo Hyvärinen,et al. DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model , 2011, J. Mach. Learn. Res..
[6] M. Kendall. Statistical Methods for Research Workers , 1937, Nature.
[7] Bernhard Schölkopf,et al. Nonlinear causal discovery with additive noise models , 2008, NIPS.
[8] Le Song,et al. A Kernel Statistical Test of Independence , 2007, NIPS.
[9] Erkki Oja,et al. Independent Component Analysis , 2001 .
[10] P. Spirtes,et al. Causation, prediction, and search , 1993 .
[11] Bernhard Schölkopf,et al. Regression by dependence minimization and its application to causal inference in additive noise models , 2009, ICML '09.
[12] Patrik O. Hoyer,et al. Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models , 2010, JSAI-isAI Workshops.
[13] Patrik O. Hoyer,et al. Estimation of causal effects using linear non-Gaussian causal models with hidden variables , 2008, Int. J. Approx. Reason..