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Causal discovery from data affected by latent confounders is an important and difficult challenge.
G. Darmois, Analyse générale des liaisons stochastiques: etude particuliére de l’analyse factorielle linéaire, Review of the International Statistical Institute 21 (1953) 2–8
1953
Earlier work this paper cites.
V. P. Skitovitch, On a property of the normal distribution, Doklady Akademii Nauk SSSR 89 (1953) 217–219
1953
Earlier work this paper cites.
S. S. Shapiro, M. B. Wilk, An analysis of variance test for normality (complete samples), Biometrika 52 (3/4) (1965) 591–611
1965
Earlier work this paper cites.
1972
Earlier work this paper cites.
doi:10.1007/BF01589116
D. C. Liu, J. Nocedal, On the limited memory BFGS method for large scale optimization , Mathematical Programming 45 (1) (1989) 503–528 · 1989
Earlier work this paper cites.
P. Spirtes, C. Glymour, An algorithm for fast recovery of sparse causal graphs, Social Science Computer Review 9 (1) (1991) 62–72
1991
Earlier work this paper cites.
J. Pearl, Comment: Graphical models, causality and intervention, Statistical Science 8 (3) (1993) 266–269
1993
Earlier work this paper cites.
P. Spirtes, C. Meek, T. Richardson, Causal discovery in the presence of latent variables and selection bias, in: G. F. Cooper, C. N. Glymour (Eds.), Computation, Causality, and Discovery, AAAI Press, 1999, pp. 211–252
1999
Earlier work this paper cites.
J. Pearl, Causality: models, reasoning and inference, Cambridge University Press, 2000
2000
Cited alongside, same era.
D. M. Chickering, Optimal structure identification with greedy search, Journal of machine learning research 3 (Nov) (2002) 507–554
2002
Cited alongside, same era.
doi:10.1198/016214506000000735
H. Zou, The adaptive lasso and its oracle properties, Journal of the American Statistical Association 101 (476) (2006) 1418–1429 · 2006
Cited alongside, same era.
doi:https://doi.org/10.1016/j.ijar.2008.02.006
P. O. Hoyer, S. Shimizu, A. J. Kerminen, M. Palviainen, Estimation of causal effects using linear non-Gaussian causal models with hidden variables, International Journal of Approximate Reasoning 49 (2) (2008) 362 – 378, special Section on Probabilistic Rough Sets and Special Section on PGM’06 · 2008
Cited alongside, same era.
A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf, A. J. Smola, A kernel statistical test of independence, in: J. C. Platt, D. Koller, Y. Singer, S. T. Roweis (Eds.), Advances in Neural Information Processing Systems 20, Curran Associates, Inc., 2008, pp. 585–592
S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer, K. Bollen, DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model, Journal of Machine Learning Research 12 (Apr) (2011) 1225–1248
2011
Later among the works it cites.
doi:10.1214/11-AOS940
D. Colombo, M. H. Maathuis, M. Kalisch, T. S. Richardson, Learning high-dimensional directed acyclic graphs with latent and selection variables , Annals of Statistics 40 (1) (2012) 294–321 · 2012
Later among the works it cites.
J. M. Ogarrio, P. Spirtes, J. Ramsey, A hybrid causal search algorithm for latent variable models, in: Conference on Probabilistic Graphical Models, 2016, pp. 368–379
2016
Later among the works it cites.
H. Zhang, S. Zhou, K. Zhang, J. Guan, Causal discovery using regression-based conditional independence tests, in: Thirty-First AAAI Conference on Artificial Intelligence, 2017
2017
Later among the works it cites.
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2008
Cited alongside, same era.
P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, B. Schölkopf, Nonlinear causal discovery with additive noise models, in: D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.), Advances in Neural Information Processing Systems 21, Curran Associates, Inc., 2009, pp. 689–696
2009
Cited alongside, same era.
doi:10.1145/1553374.1553470
J. Mooij, D. Janzing, J. Peters, B. Schölkopf, Regression by dependence minimization and its application to causal inference in additive noise models, in: Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, ACM, New York, NY, USA, 2009, pp. 745–752 · 2009
Cited alongside, same era.
M. Yamada, M. Sugiyama, Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise, in: Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010
2010
Cited alongside, same era.
T. N. Maeda, S. Shimizu, RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders, in: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS2020), 2020, pp. 735–745
2020
Closest in time.
S. Shimizu, P. O. Hoyer, A. Hyvärinen, A. Kerminen, A linear non-Gaussian acyclic model for causal discovery, Journal of Machine Learning Research 7 (Oct) (2006) 2003–2030
2030
Closest in time.
J. Peters, J. M. Mooij, D. Janzing, B. Schölkopf, Causal discovery with continuous additive noise models, The Journal of Machine Learning Research 15 (1) (2014) 2009–2053
2053
Closest in time.