On Shapley value for measuring importance of dependent inputs

Art Owen 1 Clémentine Prieur 2, 3
3 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble
Abstract : This paper makes the case for using Shapley value to quantify the importance of random input variables to a function. Alternatives based on the ANOVA decomposition can run into conceptual and computational problems when the input variables are dependent. Our main goal here is to show that Shapley value removes the conceptual problems. We do this with some simple examples where Shapley value leads to intuitively reasonable nearly closed form values.
Type de document :
Article dans une revue
SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2017, 51 (1), pp.986-1002. 〈10.1137/16M1097717〉
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https://hal.archives-ouvertes.fr/hal-01379188
Contributeur : Clémentine Prieur <>
Soumis le : mardi 21 mars 2017 - 21:07:29
Dernière modification le : samedi 10 novembre 2018 - 01:14:30
Document(s) archivé(s) le : jeudi 22 juin 2017 - 14:18:43

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Art Owen, Clémentine Prieur. On Shapley value for measuring importance of dependent inputs. SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2017, 51 (1), pp.986-1002. 〈10.1137/16M1097717〉. 〈hal-01379188v3〉

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