Abstract Event Causal Rules: Induction and Application

抽象事件因果规则：归纳与应用

Abstract: Event-centric intelligent analytical systems heavily depend on explicit causal event knowledge for risk early warning, decision-making support and narrative comprehension. Nevertheless, existing instance-level causal pairs suffer severe generalization deficits on low-frequency long-tail and unseen event combinations.

摘要： 以事件为中心的智能分析系统在风险预警、决策支持和叙事理解方面，高度依赖显式的因果事件知识。然而，现有的实例级因果对在处理低频长尾事件和未见过的事件组合时，存在严重的泛化能力不足问题。

To address this limitation, this work proposes Abstract Event Causal Rule (AECR), a novel relation-level causal abstraction paradigm that transforms concrete cause-effect pairs into generalized abstract causal logic while retaining their intrinsic causal relationships.

为了解决这一局限性，本文提出了抽象事件因果规则（AECR），这是一种新颖的关系级因果抽象范式。它将具体的因果对转化为广义的抽象因果逻辑，同时保留了它们内在的因果关系。

We design a multi-agent Concrete-to-Abstract Causal Induction (CACI) system coupled with similarity-constrained clustering to distill trustworthy AECRs from noisy raw causal data, based on which two complete AECR knowledge bases are built.

我们设计了一个多智能体“具体到抽象因果归纳”（CACI）系统，并结合相似度约束聚类，从嘈杂的原始因果数据中提取可信的 AECR，并在此基础上构建了两个完整的 AECR 知识库。

To validate the practical utility of abstract causal knowledge, we propose an Abstract Rule-Guided Causal Attention Encoder (AR-GCAE), which injects the retrieved AECRs into the causality Graph Event Prediction (CGEP) benchmark task via rule-guided attention layers and gated representation fusion.

为了验证抽象因果知识的实际效用，我们提出了抽象规则引导的因果注意力编码器（AR-GCAE），通过规则引导的注意力层和门控表示融合，将检索到的 AECR 注入到因果图事件预测（CGEP）基准任务中。

Quantitative experimental results reveal that applying AECRs substantially strengthens the generalization capacity of event causal reasoning and brings consistent performance improvements to event prediction, with the most prominent gains observed on rare and unseen event samples.

定量实验结果表明，应用 AECR 能够显著增强事件因果推理的泛化能力，并为事件预测带来持续的性能提升，其中在罕见和未见过的事件样本上观察到的增益最为显著。