Causal discovery methods based on constrained functional causal models

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], VAR-LiNGAM [3], RCD [4], and CAM-UV [5]), post-nonlinear (PNL [6]) causal models, and additive noise models (ANM [7]).

Contents:

• LiNGAM-based Methods
  • ICA-based LiNGAM
    • Parameters
    • Returns
  • DirectLiNGAM
    • Parameters
    • Returns
  • VAR-LiNGAM
    • Parameters
    • Returns
  • RCD
    • Parameters
    • Returns
  • CAM-UV
    • Parameters
    • Returns
• Post-nonlinear causal models
  • Algorithm Introduction
  • Usage
  • Parameters
  • Returns
• Additive noise models
  • Algorithm Introduction
  • Usage
  • Parameters
  • Returns

[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]

Hyvärinen, A., Zhang, K., Shimizu, S., & Hoyer, P. O. (2010). Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 11(5).

[4]

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.

[5]

Maeda, T. N., & Shimizu, S. (2021). Causal Additive Models with Unobserved Variables. UAI.

[6]

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.

[7]

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).