A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals
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.
Cite
Text
Aguilera-Ventura et al. "A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/479Markdown
[Aguilera-Ventura et al. "A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/aguileraventura2025ijcai-non/) doi:10.24963/IJCAI.2025/479BibTeX
@inproceedings{aguileraventura2025ijcai-non,
title = {{A Non-Interventionist Approach to Causal Reasoning Based on Lewisian Counterfactuals}},
author = {Aguilera-Ventura, Carlos and Liu, Xinghan and Lorini, Emiliano and Rozplokhas, Dmitry},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {4301-4310},
doi = {10.24963/IJCAI.2025/479},
url = {https://mlanthology.org/ijcai/2025/aguileraventura2025ijcai-non/}
}