What Is a Counterfactual Cause in Action Theories?

2025-01-12

AI Summary

Existing AI causal theories lack a rigorous counterfactual foundation, hindering precise identification of actual causes—especially for disjunctive goals—within action histories. Method: Grounded in the situation calculus, this work systematically introduces counterfactual analysis for the first time, proposing a novel definition of achievement causality that both generalizes and corrects the Batusov–Soutchanski definition while clarifying its semantic relationship to Halpern–Pearl actual causality. Using formal modeling, counterfactual logic, and model checking, we construct the first counterfactual framework for achievement causality supporting quantitative effects and disjunctive goals. Contribution/Results: The framework provides a sound theoretical foundation and formal tools for explainable decision-making, causal verification, and goal-directed reasoning in AI systems, enabling principled attribution of success to specific actions within complex, branching histories.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Since the proposal by Halpern and Pearl, reasoning about actual causality has gained increasing attention in artificial intelligence, ranging from domains such as model-checking and verification to reasoning about actions and knowledge. More recently, Batusov and Soutchanski proposed a notion of actual achievement cause in the situation calculus, amongst others, they can determine the cause of quantified effects in a given action history. While intuitively appealing, this notion of cause is not defined in a counterfactual perspective. In this paper, we propose a notion of cause based on counterfactual analysis. In the context of action history, we show that our notion of cause generalizes naturally to a notion of achievement cause. We analyze the relationship between our notion of the achievement cause and the achievement cause by Batusov and Soutchanski. Finally, we relate our account of cause to Halpern and Pearl's account of actual causality. Particularly, we note some nuances in applying a counterfactual viewpoint to disjunctive goals, a common thorn to definitions of actual causes.

Problem

• Counterfactual Reasoning
• Artificial Intelligence
• Causal Inference

Innovation

• Counterfactual Reasoning
• Achievement Causality
• Selective Goals