A Non-Interventionist Approach to Causal Reasoning based on Lewisian Counterfactuals

2025-05-19

AI Summary

This paper addresses the foundational reliance on “intervention” in causal inference by proposing a non-interventional framework for actual causality grounded in Lewis’s counterfactual semantics. Methodologically, it decomposes system states into propositional valuations and a causal basis, enabling a formal similarity relation between states—thereby defining actual causes without external interventions. Contributions include: (i) the first rigorous definition of actual causality within a purely counterfactual semantic framework; (ii) a computable model that encodes causal reasoning via propositional logic and counterfactual semantics, reducing causal verification to Quantified Boolean Formula (QBF) solving; and (iii) a complexity-theoretic result establishing that the corresponding model-checking problem is PSPACE-complete, alongside an automated verification toolchain implementation. The framework thus bridges philosophical rigor with computational tractability in actual causality analysis.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Formalizing actual cause without intervention using counterfactuals
• Decomposing state into propositional valuation and causal base
• PSPACE-complete model checking for counterfactual conditionals

Innovation

• Decomposes state into valuation and causal base
• Computes similarity from state components
• Reduces model checking to QBF for verification