Causal discovery methods based on constrained functional causal models
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In this section, we would like to introduce causal discovery methods based on constrained functional causal models. Now we have LiNGAM-based methods
(ICA-based LiNGAM [1]_, DirectLiNGAM [2]_, RCD [3]_, and CAM-UV [4]_), post-nonlinear (PNL [5]_) causal models, and additive noise models (ANM [6]_).


Contents:

.. toctree::
    :maxdepth: 3

    lingam
    pnl
    anm

.. [1] Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., & Jordan, M. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(10).
.. [2] Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., ... & Bollen, K. (2011). DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. The Journal of Machine Learning Research, 12, 1225-1248.
.. [3] Maeda, T. N., & Shimizu, S. (2020, June). RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. In International Conference on Artificial Intelligence and Statistics (pp. 735-745). PMLR.
.. [4] Maeda, T. N., & Shimizu, S. (2021). Causal Additive Models with Unobserved Variables. UAI.
.. [5] Zhang, K., & Hyvärinen, A. (2009, June). On the Identifiability of the Post-Nonlinear Causal Model. In 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009) (pp. 647-655). AUAI Press.
.. [6] Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2008, December). Nonlinear causal discovery with additive noise models. In NIPS (Vol. 21, pp. 689-696).