[41] Albert-Laszlo Barabasi and Reka Albert. Emergence of scaling in random networks. Science, 286(5439):509-512, 1999. doi: 10.1126/science.286.5439.509. URL http://www.sciencemag.org/cgi/content/abstract/286/5439/509.
[42] Ulrik Brandes, Marco Gaertler, and Dorothea Wagner. Experiments on graph clustering algorithms. In Giuseppe Di Battista and Uri Zwick, editors, Algorithms - ESA 2003, pages 568-579, Berlin, Heidelberg, 2003. Springer Berlin Heidelberg. ISBN 978-3-540-39658-1.
[43] D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. Adaptive computation and machine learning. MIT Press, 2009. ISBN 9780262013192. URL https://books.google.co.in/books?id=7dzpHCHzNQ4C.
[44] Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik Hoyer, and Kenneth Bollen. Directlingam: A direct method for learning a linear non-gaussian structural equation model. Journal of Machine Learning Research, 12, 01 2011.
[45] Pedro Sanchez, Xiao Liu, Alison Q O'Neil, and Sotirios A. Tsaftaris. Diffusion models for causal discovery via topological ordering. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=Idusfje4-Wq.
[46] Peter Buhlmann, Jonas Peters, and Jan Ernest. CAM: Causal additive models, high-dimensional order search and penalized regression. The Annals of Statistics, 42(6), dec 2014. URL https://doi.org/10.1214%2F14-aos1260.
[47] Jiji Zhang. Causal reasoning with ancestral graphs. Journal of Machine Learning Research, 9 (47):1437-1474, 2008. URL http://jmlr.org/papers/v9/zhang08a.html.
[48] Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing. Learning sparse nonparametric dags. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 3414-3425. PMLR, 26-28 Aug 2020. URL https://proceedings.mlr.press/v108/zheng20a.html.
[49] Alexander G. Reisach, Myriam Tami, Christof Seiler, Antoine Chambaz, and Sebastian Weichwald. Simple sorting criteria help find the causal order in additive noise models, 2023.
[50] Alexander Gilbert Reisach, Christof Seiler, and Sebastian Weichwald. Beware of the simulated DAG! causal discovery benchmarks may be easy to game. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=wE0lVzVhMW_.
[51] Thomas Verma and Judea Pearl. Causal networks: Semantics and expressiveness. In Ross D. Shachter, Tod S. Levitt, Laveen N. Kanal, and John F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 9 of Machine Intelligence and Pattern Recognition, pages 69-76. North-Holland, 1990. doi: https://doi.org/10.1016/B978-0-444-88650-7.50011-1. URL https://www.sciencedirect.com/science/article/pii/B9780444886507500111.
[52] Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Kernel-based conditional independence test and application in causal discovery. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI'11, pages 804-813, Arlington, Virginia, USA, July 2011. AUAI Press.
[53] Anthony C. Constantinou. Evaluating structure learning algorithms with a balanced scoring function. CoRR, abs/1905.12666, 2019. URL http://arxiv.org/abs/1905.12666.
[54] Jiji Zhang and Peter Spirtes. Strong faithfulness and uniform consistency in causal inference. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, UAI'03, page 632-639, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc. ISBN 0127056645.