How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs

2025-07-07

AI Summary

This work addresses the challenge of modeling causal intervention effects in logic programs. We propose Hierarchical Abductive Logic Programming (HALP), a framework that couples logical rules with formal causal semantics: logical programs are formally translated into Pearl’s structural causal models (SCMs), and endowed with stable model semantics satisfying philosophical principles including causal sufficiency and natural necessity. HALP is the first logic programming framework to rigorously distinguish causal knowledge from descriptive knowledge, thereby enabling sound counterfactual and do-intervention reasoning. Empirical evaluation demonstrates the framework’s computability for do-calculus operations and its predictive validity. Our main contributions are: (1) the first abductive logic programming system equipped with rigorous causal semantics; (2) semantic alignment between logical rules and structural causal models; and (3) a verifiable, executable logical foundation for AI-based causal reasoning.

Technology Category

• Knowledge Representation and Reasoning: Logic Programming
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Pearl observes that causal knowledge enables predicting the effects of interventions, such as actions, whereas descriptive knowledge only permits drawing conclusions from observation. This paper extends Pearl's approach to causality and interventions to the setting of stratified abductive logic programs. It shows how stable models of such programs can be given a causal interpretation by building on philosophical foundations and recent work by Bochman and Eelink et al. In particular, it provides a translation of abductive logic programs into causal systems, thereby clarifying the informal causal reading of logic program rules and supporting principled reasoning about external actions. The main result establishes that the stable model semantics for stratified programs conforms to key philosophical principles of causation, such as causal sufficiency, natural necessity, and irrelevance of unobserved effects. This justifies the use of stratified abductive logic programs as a framework for causal modeling and for predicting the effects of interventions

Problem

• Extends Pearl's causality to abductive logic programs
• Translates logic programs into causal systems
• Validates stable model semantics for causal principles

Innovation

• Extends Pearl's causality to abductive logic programs
• Translates logic programs into causal systems
• Conforms stable models to philosophical causation principles