2008-07-09-kuroki08a.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
The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification	This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/outcome variable in many practical fields.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kuroki08a	0	The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification	333	340	333-340	333	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Kuroki, Manabu and Cai, Zhihong	given family Manabu Kuroki given family Zhihong Cai	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/kuroki08a/kuroki08a.pdf