2025/11/20 by Guus Eelink, Kilian Rückschloß, Felix Weitkämper
Arts and Humanities · Mathematics · #Classical Philosophy and Thought #Education, Psychology, and Complexity Research #Historical Philosophy and Science
paper · doi:10.1016/j.ijar.2025.109603
openalex created_date 2025/11/20 · openalex publication_date 2025/11/20 · openalex updated_date 2026/07/22
Bayesian networks and causal models provide frameworks for reasoning about external interventions, enabling tasks that go beyond what probability distributions alone can support. Although these formalisms are often informally described as encoding causal knowledge, there is a lack of a formal theory that characterizes the kind of knowledge required to predict the effects of such interventions. This work introduces the theoretical framework of causal systems to implement Aristotle’s distinction between knowledge- that and knowledge- why within the setting of artificial intelligence. By interpreting existing AI technologies as causal systems, it examines the corresponding forms of knowledge they embody. Finally, it argues that predicting the effects of external interventions is possible only with knowledge- why , offering a more precise account of the assumptions underlying this capacity.