[20] Weihao Gao, Sewoong Oh, and Pramod Viswanath. Demystifying fixed  $k$ -nearest neighbor information estimators. In Information Theory (ISIT), 2017 IEEE International Symposium on, pages 1267-1271, 2017.
[21] Ian Goodfellow, *Yoshua Bengio*, and Aaron Courville. *Deep learning*. MIT Press, 2016.
[22] Guido W Imbens and Donald B Rubin. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
[23] Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, and James m Robins. Nonparametric von mises estimators for entropies, divergences and mutual informations. In Advances in Neural Information Processing Systems, 28, 2015.
[24] Eugene Katsevich and Chiara Sabatti. Multilayer knockoff filter: Controlled variable selection at multiple resolutions. The Annals of Applied Statistics, 13(1):1-33, 2019.
[25] Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan, and Larry Wasserman. Local permutation tests for conditional independence. The Annals of Statistics, 50(6):3388-3414, 2022.
[26] Daphne Koller and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT Press, 2009.
[27] Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. Estimating mutual information. Physical Review E, 69(6):066138, 2004.
[28] Chun Li and Xiaodan Fan. On nonparametric conditional independence tests for continuous variables. Wiley Interdisciplinary Reviews: Computational Statistics, 12(3):e1489, 2020.
[29] Honghao Li, Vincent Cabeli, Nadir Sella, and Hervé Isambert. Constraint-based causal structure learning with consistent separating sets. In Advances in Neural Information Processing Systems, 32, 2019.
[30] Shuai Li, Ziqi Chen, Hongtu Zhu, Christina Dan Wang, and Wang Wen. Nearest-neighbor sampling based conditional independence testing. arXiv preprint arXiv:2304.04183, 2023.
[31] Dimitris Margaritis. Distribution-free learning of bayesian network structure in continuous domains. In Proceedings of the 20th National Conference on Artificial Intelligence, volume 5, pages 825-830, 2005.
[32] Arnab Mondal, Arnab Bhattacharjee, Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan, and AP Prathosh. C-mi-gan: Estimation of conditional mutual information using minmax formulation. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, pages 849-858, 2020.
[33] Joris M Mooij and Tom Heskes. Cyclic causal discovery from continuous equilibrium data. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, pages 431-439, 2013.
[34] Sudipto Mukherjee, Himanshu Asnani, and Sreeram Kannan. Ccmi: Classifier based conditional mutual information estimation. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, pages 1083-1093, 2020.
[35] Matey Neykov, Sivaraman Balakrishnan, and Larry Wasserman. Minimax optimal conditional independence testing. The Annals of Statistics, 49(4):2151-2177, 2021.
[36] Ignavier Ng, AmirEmad Ghassami, and Kun Zhang. On the role of sparsity and dag constraints for learning linear dags. In Advances in Neural Information Processing Systems, 33, 2020.
[37] Judea Pearl. Causality. Cambridge University Press, 2009.
[38] Jakob Runge. Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. In Proceedings of the 21th International Conference on Artificial Intelligence and Statistics, pages 938–947, 2018.