[1] B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, “Towards causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5, pp. 612-634, May 2021.
[2] J. Pearl and D. Mackenzie, The Book of Why: The New Science of Cause and Effect, New York, Basic Books, 2018.
[3] J. Peters, D. Janzing, and B. Scholkopf, Elements of Causal Inference: Foundations and Learning Algorithms, The MIT Press, 2017.
[4] M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, and J. Wang, “CausalVAE: Disentangled representation learning via neural structural causal models,” in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[5] F. Locatello, B. Poole, G. Ratsch, B. Scholkopf, O. Bachem, and M. Tschannen, “Weakly-supervised disentanglement without compromises,” in Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
[6] H. Kim and A. Mnih, “Disentangling by factorizing,” in Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
[7] S. Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are disentangled representations helpful for abstract visual reasoning?” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2019.
[8] J. Kugelgen, A. Mey, M. Loog, and B. Scholkopf, “Semi-supervised learning, causality, and the conditional cluster assumption,” in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR, vol. 124, 2020.
