Discovering cyclic causal models by independent components analysis

Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:366-374, 2008.

Abstract

We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is "stable".

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-lacerda08a, title = {Discovering cyclic causal models by independent components analysis}, author = {Lacerda, Gustavo and Spirtes, Peter and Ramsey, Joseph and Hoyer, Patrik O.}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {366--374}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/lacerda08a/lacerda08a.pdf}, url = {https://proceedings.mlr.press/r6/lacerda08a.html}, abstract = {We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is "stable".}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Discovering cyclic causal models by independent components analysis %A Gustavo Lacerda %A Peter Spirtes %A Joseph Ramsey %A Patrik O. Hoyer %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-lacerda08a %I PMLR %P 366--374 %U https://proceedings.mlr.press/r6/lacerda08a.html %V R6 %X We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is "stable". %Z Reissued by PMLR on 09 October 2024.
APA
Lacerda, G., Spirtes, P., Ramsey, J. & Hoyer, P.O.. (2008). Discovering cyclic causal models by independent components analysis. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:366-374 Available from https://proceedings.mlr.press/r6/lacerda08a.html. Reissued by PMLR on 09 October 2024.

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