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2020 | OriginalPaper | Chapter

Causality in Probabilistic Fuzzy Logic and Alternative Causes as Fuzzy Duals

Authors : Serge Robert, Usef Faghihi, Youssef Barkaoui, Nadia Ghazzali

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

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Abstract

Causal inference is an essential part of human reasoning. Computer scientists often use inferential logic and probability theories to find the causes of the events. In causal inference, researchers are interested in finding the relationship between two observable events. However, most of the time, we can only perceive the co-occurrences of events or usual successions of events. So, we must be careful in trying to establish causality between events. Also, many alternative causes or conjoint causes can be at work. So, we need good tools to deal with the complex problem of causality. In this paper, we will explore probabilistic fuzzy logic (PFL) as a sophisticated alternative to establish causal inference and fuzzy duals as tools for the expression of alternative possible causes.

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Metadata
Title
Causality in Probabilistic Fuzzy Logic and Alternative Causes as Fuzzy Duals
Authors
Serge Robert
Usef Faghihi
Youssef Barkaoui
Nadia Ghazzali
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-63119-2_62

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