Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models

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Abstract

Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalisms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. We show that the standard restriction to so-called recursive models (with no cycles in the dependency graph) is not necessary in our approach, allowing us to reason about mutually dependent processes and feedback loops. Finally, we introduce new notions of model equivalence for temporal causal models, and show that CPLTL has an efficient model-checking procedure.

Original languageEnglish
Title of host publicationSpecial Track on AI Alignment
EditorsToby Walsh, Julie Shah, Zico Kolter
PublisherAssociation for the Advancement of Artificial Intelligence
Pages14949-14957
Number of pages9
Edition14
ISBN (Print)9781577358978
DOIs
Publication statusPublished - 11 Apr 2025
Event39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number14
Volume39
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Country/TerritoryUnited States
CityPhiladelphia
Period25/02/254/03/25

Bibliographical note

Publisher Copyright:
Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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