Arrow Research search
Back to TARK

TARK 2025

Causality Without Causal Models

Conference Paper Conference and Workshop Papers Artificial Intelligence ยท Logic in Computer Science

Abstract

Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined. By abstracting the definition, we gain a number of benefits. Not only can we apply the definition in a wider range of models, including ones that allow, for example, backtracking, but we can apply the definition to determine if A is a cause of B even if A and B are formulas involving disjunctions, negations, beliefs, and nested counterfactuals (none of which can be handled by the Halpern-Pearl definition). Moreover, we can extend the ideas to getting an abstract definition of explanation that can be applied beyond causal models. Finally, we gain a deeper understanding of features of the definition even in causal models.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
Conference on Theoretical Aspects of Rationality and Knowledge
Archive span
1986-2025
Indexed papers
500
Paper id
367847527179525728
v2026.09.13