Abstract
Bayesian networks and causal models provide frameworks for handling queries about external interventions and counterfactuals, enabling tasks that go beyond what probability distributions alone can address. While these formalisms are often informally described as capturing causal knowledge, there is a lack of a formal theory characterizing the type of knowledge required to predict the effects of external interventions. This work introduces the theoretical framework of causal systems to clarify Aristotle's distinction between knowledge that and knowledge why within artificial intelligence. By interpreting existing artificial intelligence technologies as causal systems, it investigates the corresponding types of knowledge. Furthermore, it argues that predicting the effects of external interventions is feasible only with knowledge why, providing a more precise understanding of the knowledge necessary for such tasks.
Abstract (translated)
贝叶斯网络和因果模型为处理关于外部干预及反事实的查询提供了框架，能够完成超出概率分布本身所不能解决的任务。尽管这些形式主义常被非正式地描述为捕捉因果知识，但缺乏一个正式理论来刻画预测外部干预效果所需的知识类型。这项工作引入了因果系统的理论框架，以阐明亚里士多德关于“知道什么”和“知道为什么”的区分在人工智能领域的应用。通过将现有的人工智能技术解释为因果系统，该研究探讨了相应的知识类型。此外，它论证了只有具备“知道为什么”的知识才能预测外部干预的效果，从而对完成此类任务所需的知识提供了一个更精确的理解。 这一理论框架有助于澄清在处理复杂问题时，理解因果关系的重要性，并强调了仅仅拥有数据和概率模型不足以进行有效的决策或推理；需要深入理解事件之间的因果机制。这为人工智能领域内的研究开辟了一条新的路径，即不仅要关注“是什么”，更要重视“为什么”的探索。
URL
https://arxiv.org/abs/2504.02430
