用逻辑程序建模因果关系，能准确预测干预效果
How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs
- 将分层可假设逻辑程序转化为因果系统，赋予规则因果意义
- 稳定模型满足因果充分性等核心哲学原则，推理更可靠
- 适合需要严谨因果推理的领域，如医疗诊断与决策系统
Pearl指出，因果知识可预测干预（如行动）的影响，而描述性知识仅能基于观察推断。本文将这一因果框架拓展至分层可假设逻辑程序，通过哲学基础及Bochman、Eelink等人的近期工作，证明此类程序的稳定模型可赋予因果解释。具体而言，提出将逻辑程序规则转化为因果系统，澄清了规则的非正式因果含义，并支持对外部动作的合理推理。主要结论表明，分层程序的稳定模型语义符合因果性的关键哲学原则，如因果充分性、自然必要性以及未观测效应的无关性。这为使用分层可假设逻辑程序进行因果建模和干预效果预测提供了理论依据。
原文摘要 · Abstract (English)
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
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