Dynamic Causality

Publication date

2023-09-28

Authors

Gladyshev, MaksimORCID 0000-0002-6657-4870ISNI 0000000523483460
Alechina, NatashaORCID 0000-0003-3306-9891ISNI 0000000124421545
Dastani, MehdiISNI 0000000043464658
Doder, DraganISNI 0000000506363539
Logan, BrianORCID 0000-0003-0648-7107ISNI 0000000124462996

Editors

Gal, Kobi
Gal, Kobi
Nowe, Ann
Nalepa, Grzegorz J.
Fairstein, Roy
Radulescu, Roxana

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

cc_by_nc

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.

Keywords

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