bibtype	C - Conference Paper (international conference)
ARLID	0507223
utime	20240103222349.9
mtime	20190803235959.9
WOS	000418391500002
title (primary) (eng)	Causality and Intervention in Business Process Management
specification	page_count 10 s. media_type P
serial	ARLID cav_un_epca*0480157 ISBN 978-80-7464-932-5 title Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty page_num 15-24 publisher place Ostrava name University of Ostrava year 2017 editor name1 Novák name2 V. editor name1 Inuiguchi name2 M. editor name1 Štěpnička name2 M.
keyword	Compositional model
keyword	Operator of composition,
keyword	Causality
keyword	Conditioning
keyword	Intervention
author (primary)	ARLID cav_un_auth*0095925 share 50 name1 Bína name2 V. country CZ
author	ARLID cav_un_auth*0101118 full_dept Department of Decision Making Theory share 50 name1 Jiroušek name2 Radim institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept Department of Decision Making Theory department (cz) MTR department MTR garant K fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2019/MTR/jirousek-0507223.pdf
cas_special	project ARLID cav_un_auth*0353269 project_id GA15-00215S agency GA ČR country CZ abstract (eng) The paper presents an algebraic approach to the modeling of causality in systems of stochastic variables. The methodology is based on an operator of a composition that provides the possibility of composing a multidimensional distribution from low-dimensional building blocks taking advantage of the dependence structure of the problem variables. The authors formally define and demonstrate on a hypothetical example a surprisingly elegant unifying approach to conditioning by a single variable and the evaluation of the effect of an intervention. Both operations are realized by the composition with a degenerated distribution and differ only in the sequence in which the operator of the composition is performed. action ARLID cav_un_auth*0355859 name Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /20./ dates 20170917 mrcbC20-s 20170920 place Pardubice country CZ RIV IN FORD0 10000 FORD1 10200 FORD2 10201 reportyear 2020 num_of_auth 2 presentation_type PR inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0298570 confidential S mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied mrcbC86 n.a. Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied arlyear 2017 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 000418391500002 WOS mrcbU63 cav_un_epca*0480157 Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty University of Ostrava 2017 Ostrava 15 24 978-80-7464-932-5 mrcbU67 340 Novák V. mrcbU67 340 Inuiguchi M. mrcbU67 340 Štěpnička M.