Y. Jiang and B. Aragam. Learning nonparametric latent causal graphs with unknown interventions, 2023.
I. Khemakhem, D. Kingma, R. Monti, and A. Hyvarinen. Variational autoencoders and nonlinear ica: A unifying framework. In International Conference on Artificial Intelligence and Statistics, pages 2207-2217. PMLR, 2020.
B. Kivva, G. Rajendran, P. Ravikumar, and B. Aragam. Learning latent causal graphs via mixture oracles. Advances in Neural Information Processing Systems, 34, 2021.
L. Kong, S. Xie, W. Yao, Y. Zheng, G. Chen, P. Stojanov, V. Akinwande, and K. Zhang. Partial disentanglement for domain adaptation. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 11455-11472. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/kong22a.html.
E. Kummerfeld and J. Ramsey. Causal clustering for 1-factor measurement models. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1655-1664. ACM, 2016.
S. Lachapelle, P. R. López, Y. Sharma, K. Everett, R. L. Priol, A. Lacoste, and S. Lacoste-Julien. Disentanglement via mechanism sparsity regularization: A new principle for nonlinearica, 2022.
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR 2011, pages 3361-3368. IEEE, 2011.
W. Liang, A. Kekic, J. von Kugelgen, S. Buchholz, M. Besserve, L. Gresele, and B. Schölkopf. Causal component analysis, 2023.
Q. Lyu, X. Fu, W. Wang, and S. Lu. Understanding latent correlation-based multiview learning and self-supervision: An identifiability perspective. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=5FUq05QRc5b.
J. Pearl. *Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference*. Morgan Kaufmann, 1988.
J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, NY, USA, 2000. ISBN 0-521-77362-8.
J. Peters, D. Janzing, and B. Schölkopf. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, Cambridge, MA, USA, 2017.
O.-S. P. Project. Big five personality test, 2019. URL https://openpsychometrics.org/tests/ IPIP-BFFM/. Accessed: 2023-5-16.
S. Shimizu, P. O. Hoyer, and A. Hyvarinen. Estimation of linear non-gaussian acyclic models for latent factors. Neurocomputing, 72(7-9):2024-2027, 2009.
R. Silva, R. Scheine, C. Glymour, and P. Spirtes. Learning the structure of linear latent variable models. Journal of Machine Learning Research, 7(Feb):191-246, 2006a.
R. Silva, R. Scheines, C. Glymour, and P. Spirtes. Learning the structure of linear latent variable models. Journal of Machine Learning Research, 7:191-246, 2006b.
P. Spirtes, C. N. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT press, 2000.
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, Cambridge, MA, 2nd edition, 2001.
C. Squires, A. Seigal, S. Bhate, and C. Uhler. Linear causal disentanglement via interventions, 2023.
F. Theis. Towards a general independent subspace analysis. Advances in Neural Information Processing Systems, 19:1361-1368, 2006.