Reasoning About Actual Causality in Answer Set Programming

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.