Causal Identification with Additive Noise Models: Quantifying the Effect of Noise

Reference : Causal Identification with Additive Noise Models: Quantifying the Effect of Noise


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/49343

Title :	Causal Identification with Additive Noise Models: Quantifying the Effect of Noise
Language :	English
Author, co-author :	Kap, Benjamin [University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM) > >]
	Aleksandrova, Marharyta [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	2021
Peer reviewed :	Yes
Audience :	International
Event name :	10èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB-2021)
Event date :	from 11-10-2021 to 12-10-2021
Permalink :	http://hdl.handle.net/10993/49343
Other URL :	https://arxiv.org/abs/2110.08087

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Open access	JFRB_2021.pdf		Publisher postprint	450.17 kB	View/Open

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