将因果推理引入概率逻辑编程，实现干预效果的精准预测。
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
- 基于哲学基础构建无时间顺序的因果语义框架
- 在分层ProbLog上与现有P-log语义一致，非分层时可差异
- 为逻辑编程提供可解释的因果建模新方法，适合理论研究者
Pearl指出，因果知识能预测干预效果，而仅描述性知识只能基于观察推断。然而，其因果理论局限于贝叶斯网络和因果模型，主要处理无环因果关系，难以迁移至其他形式系统，易引发误解或不一致。本文将Pearl的因果思想引入概率逻辑编程（PLP）。为此，程序基于先前研究建立的哲学基础，不依赖时间概念，假设所有相关事件同时发生。提出该类程序的形式化因果语义、干预定义及实现方案。证明该语义在分层ProbLog程序上与P-log语义一致；但在非分层情形及其他PLP形式系统中可能不同。
原文摘要 · Abstract (English)
Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observations. His theory of causality, however, is developed exclusively within Bayesian networks and causal models. Consequently, it is largely restricted to acyclic causal relationships, and transferring its ideas to other formalisms risks misinterpretation or inconsistency. This paper brings Pearl's approach to causality into probabilistic logic programming (PLP). To this end, such programs are aligned with philosophical foundations established in prior work that do not rely on temporal notions; that is, all relevant events are assumed to occur simultaneously. A formal causal semantics for these programs, together with a notion of intervention and an implementation, is proposed. It is shown that this semantics coincides with the P-log semantics for stratified ProbLog programs, while the two may differ in the non-stratified case and for other PLP formalisms.
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