2008-07-09-hoyer08a.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
Causal discovery of linear acyclic models with arbitrary distributions	An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on conditional independencies (Spirtes et al. 1993; Pearl 2000) cannot distinguish between independence-equivalent models, whereas approaches purely based on Independent Component Analysis (Shimizu et al. 2006) are inapplicable to data which is partially Gaussian. In this paper, we generalize and combine the two approaches, to yield a method able to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible. We give exact graphical conditions for when two distinct models represent the same family of distributions, and empirically demonstrate the power of our method through thorough simulations.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hoyer08a	0	Causal discovery of linear acyclic models with arbitrary distributions	282	289	282-289	282	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Hoyer, Patrik O. and Hyv\"{a}rinen, Aapo and Scheines, Richard and Spirtes, Peter and Ramsey, Joseph and Lacerda, Gustavo and Shimizu, Shohei	given family Patrik O. Hoyer given family Aapo Hyvärinen given family Richard Scheines given family Peter Spirtes given family Joseph Ramsey given family Gustavo Lacerda given family Shohei Shimizu	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/hoyer08a/hoyer08a.pdf