Reasoning about actual causality in answer set programming
File(s)
Accepted version
Author(s)
Ozcan, Daniel
Alrajeh, Dalal
Craven, Robert
Type
Conference Paper
Abstract
Causal models provide a formal framework for identifying and reasoning about the causes of observed phenomena, making them valuable for decision-support contexts where understanding causality is essential. Yet applying these models in practice requires automated tools for key reasoning tasks. We present an Answer Set Programming (ASP)-based tool that supports three core capabilities for all acyclic binary causal models: (1) checking whether an event is an actual cause of another; (2) finding all minimal subsets of a failed candidate that do qualify as causes; and (3) inferring all actual causes of an outcome without assuming any candidate. Our tool is the first to support all three tasks within a unified framework, guaranteeing minimal contingency sets and outperforming prior implementations in both runtime and memory. We describe the system’s design and report on an empirical evaluation using existing benchmarks.
Date Issued
2025-11-11
Date Acceptance
2025-09-26
Citation
KR Proceedings, 2025, pp.610-620
ISBN
978-1-956792-08-9
ISSN
2334-1033
Publisher
International Joint Conferences on Artificial Intelligence Organization
Start Page
610
End Page
620
Journal / Book Title
KR Proceedings
Copyright Statement
Copyright © 2025 International Joint Conferences on Artificial Intelligence Organization
Source
22nd International Conference on Principles of Knowledge Representation and Reasoning
Publication Status
Published
Start Date
2025-11-11
Finish Date
2025-11-17
Coverage Spatial
Melbourne, Australia