Hidden causal representation learning

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:

• Generalized Independence Noise (GIN) condition-based method
  • Algorithm Introduction
  • Usage
  • Parameters
  • Returns
• Rank-based Latent Causal Discovery (RLCD)
  • Algorithm Introduction
  • Usage
  • Parameters
  • Returns

[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.