2008-07-09-cai08a.md

abstract	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Assume that cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model. We consider the identification problem of total effects in the presence of latent variables and selection bias between a treatment variable and a response variable. Pearl and his colleagues provided the back door criterion, the front door criterion (Pearl, 2000) and the conditional instrumental variable method (Brito and Pearl, 2002) as identifiability criteria for total effects in the presence of latent variables, but not in the presence of selection bias. In order to solve this problem, we propose new graphical identifiability criteria for total effects based on the identifiable factor models. The results of this paper are useful to identify total effects in observational studies and provide a new viewpoint to the identification conditions of factor models.	On identifying total effects in the presence of latent variables and selection bias	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cai08a	0	On identifying total effects in the presence of latent variables and selection bias	62	69	62-69	62	false	Cai, Zhihong and Kuroki, Manabu	given family Zhihong Cai given family Manabu Kuroki	2008-07-09	Reissued by PMLR on 09 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	https://raw.githubusercontent.com/mlresearch/r6/main/assets/cai08a/cai08a.pdf