Qualifying Causes as Pertinent

Giovanni Sileno 1, 2 J-L. Dessalles 3, 4
1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
3 DIG - Data, Intelligence and Graphs
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract :

Several computational methods have been proposed to evaluate the relevance of an instantiated cause to an observed consequence. The paper reports on an experiment to investigate the adequacy of some of these methods as descriptors of human judgments about causal relevance.

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https://hal.telecom-paristech.fr/hal-02287948
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 5:31:42 PM
Last modification on : Thursday, October 17, 2019 - 12:37:00 PM

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  • HAL Id : hal-02287948, version 1

Citation

Giovanni Sileno, J-L. Dessalles. Qualifying Causes as Pertinent. 40th Annual Conference of the Cognitive Science Society, Jul 2018, Madison, WI, United States. pp.2488-2493. ⟨hal-02287948⟩

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