@inproceedings{72de58b6ad784296870b95ea13c4cec4,
title = "Dynamic Causality",
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.",
author = "Maksim Gladyshev and Natasha Alechina and Mehdi Dastani and Dragan Doder and Brian Logan",
year = "2023",
month = sep,
day = "28",
doi = "10.3233/FAIA230355",
language = "English",
isbn = "9781643684369",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "867--874",
editor = "Kobi Gal and Kobi Gal and Ann Nowe and Nalepa, {Grzegorz J.} and Roy Fairstein and Roxana Radulescu",
booktitle = "ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings",
}