2008-07-09-lacerda08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Discovering cyclic causal models by independent components analysis	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".	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lacerda08a	0	Discovering cyclic causal models by independent components analysis	366	374	366-374	366	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Lacerda, Gustavo and Spirtes, Peter and Ramsey, Joseph and Hoyer, Patrik O.	given family Gustavo Lacerda given family Peter Spirtes given family Joseph Ramsey given family Patrik O. Hoyer	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/lacerda08a/lacerda08a.pdf