Extensions
Nonlinear models
Basic model with no latent confounders
NEW O. Faruque, S. Ali, X. Zheng, J. Wang. TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data. arXiv:2404.01466, 2024.
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Estimation of basic nonlinear models with no latent confounders
NEW N. Yin, T. Gao, Y. Yu, Q. Ji. Effective Causal Discovery under Identifiable Heteroscedastic Noise Model. In Proc. AAAI Conference on Artificial Intelligence, 38(15), 16486-16494, 2024.
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[pdf] [Google scholar]F. Montagna, N. Noceti, L. Rosasco, F. Locatello. Shortcuts for causal discovery of nonlinear models by score matching. arXiv:2310.14246, 2023.
[pdf] [Google scholar]Z. Zhu, F. Locatello, V. Cevher. Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. arXiv:2310.18123, 2024.
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[pdf] [Google scholar]H Chen, M Kaptein. A novel framework extending cause-effect inference methods to multivariate causal discovery. arXiv:2305.16904, 2023.
[pdf] [Google scholar]S. Akbari, L. Ganassali, N. Kiyavash. Learning Causal Graphs via Monotone Triangular Transport Maps. arXiv:2305.18210, 2023.
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Python codeE. Gao, I. Ng, M. Gong, L. Shen, W. Huang, T. Liu, K. Zhang, H. Bondell. MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. Arxiv preprint arXiv:2205.13869, 2022.
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Others
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