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Inexact Multiple-Grained Causal Complexes

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Data Mining: Foundations and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 118))

Summary

Causality can be imprecise as well as granular. Complete knowledge of all possible causal factors could lead to crisp causal understanding. However, knowledge of at least some causal effects is inherently inexact and imprecise. It is unlikely that complete knowledge of all possible factors can be known for many subjects. It may not be known what events are in the complex; or, what constraints and laws the complex is subject to. Consequently, causal knowledge is inherently incomplete and inexact. Whether or not all of the elements are precisely known, people recognize that a complex of elements usually causes an effect. Causal complexes are groupings of finer-grained causal relations into a larger-grained causal object. Commonsense world understanding deals with imprecision, uncertainty and imperfect knowledge. Usually, commonsense reasoning is more successful in reasoning about a fewer large-grained events than many fine-grained events. However, the larger-grained causal objects are necessarily more imprecise than some of their components. A satisficing solution might be to develop large-grained solutions and then only go to the finer-grain when the impreciseness of the large-grain is unsatisfactory.

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Mazlack, L.J. (2008). Inexact Multiple-Grained Causal Complexes. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_14

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  • DOI: https://doi.org/10.1007/978-3-540-78488-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78487-6

  • Online ISBN: 978-3-540-78488-3

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