Record link: 
http://hdl.handle.net/20.500.12708/221729
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Title: 
A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals
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Citation: 
Aguilera-Ventura, C., Liu, X., Lorini, E., & Rozplokhas, D. (2025). A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals. In J. Kwok (Ed.), Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 4301–4310). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2025/479
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Publisher DOI: 
10.24963/ijcai.2025/479
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Aguilera-Ventura, Carlos 
Liu, Xinghan 
Lorini, Emiliano 
Rozplokhas, Dmitrii 
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Organisational Unit: 
E192-03 - Forschungsbereich Knowledge Based Systems 
E192-05 - Forschungsbereich Theory and Logic 
E056-13 - Fachbereich LogiCS 
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Published in: 
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence 
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ISBN: 
978-1-956792-06-5
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Date (published): 
12-Nov-2025
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Event name: 
34th International Joint Conference on Artificial Intelligence (IJCAI) 
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Event date: 
16-Aug-2025 - 22-Aug-2025
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Event place: 
Montreal, Canada
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Number of Pages: 
10
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Publisher: 
International Joint Conferences on Artificial Intelligence
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Peer reviewed: 
Yes
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Keywords: 
Knowledge Representation and Reasoning; Causality; Uncertainty in AI; Causality; structural causal models and causal inference
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Abstract: 
We present a computationally grounded semantics for counterfactual conditionals in which i) the state in a model is decomposed into two elements: a propositional valuation and a causal base in propositional form that represents the causal information available at the state; and ii) the comparative similarity relation between states is computed from the states' two components. We show that, by means of our semantics, we can elegantly formalize the notion of actual cause without recurring to the primitive notion of intervention. Furthermore, we provide a succinct formulation of the model checking problem for a language of counterfactual conditionals in our semantics. We show that this problem is PSPACE-complete and provide a reduction of it into QBF that can be used for automatic verification of causal properties.
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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