Hidden causal representation learning
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In this section, we would like to introduce methods in hidden causal representation learning, such as
generalized independent noise (GIN [1]_) condition-based method and rank-based latent causal discovery (RLCD [2]_).


Contents:

.. toctree::
    :maxdepth: 2

    gin
    rlcd

.. [1] Xie, F., Cai, R., Huang, B., Glymour, C., Hao, Z., & Zhang, K. (2020, January). Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. In NeurIPS.
.. [2] Dong, X., Huang, B., Ng, I., Song, X., Zheng, Y., Jin, S., Legaspi, R., Spirtes, P., & Zhang, K. (2024). A versatile causal discovery framework to allow causally-related hidden variables. In International Conference on Learning Representations, vol. 2024, pp. 43084-43118.