Dynamic Causality
Publication date
2023-09-28
Editors
Gal, Kobi
Gal, Kobi
Nowe, Ann
Nalepa, Grzegorz J.
Fairstein, Roy
Radulescu, Roxana
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Supervisors
Document Type
Part of book
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Abstract
There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.
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Citation
Gladyshev, M, Alechina, N, Dastani, M, Doder, D & Logan, B 2023, Dynamic Causality. in K Gal, K Gal, A Nowe, G J Nalepa, R Fairstein & R Radulescu (eds), ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings. vol. 372, Frontiers in Artificial Intelligence and Applications, vol. 372, IOS Press, pp. 867-874. https://doi.org/10.3233/FAIA230355