# How Artificial Intelligence Leads to Knowledge Why: An Inquiry Inspired by Aristotle's Posterior Analytics

arXiv: https://arxiv.org/abs/2504.02430 · HTML: https://mlsanity.com/paper/2504.02430/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [Relational Bayesian Networks](https://mlsanity.com/paper/1302.1550/) (2013)

## Built on by (most cited first, 4 of 4)

- [How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs](https://mlsanity.com/paper/2507.05088/) (2025)
- [An Aristotelian ontology of instrumental goals: Structural features to be managed and not failures to be eliminated](https://mlsanity.com/paper/2510.25471/) (2025)
- [How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming](https://mlsanity.com/paper/2607.21208/) (2026)
- [Logic Programming Semantics for Causal Processes](https://mlsanity.com/paper/2607.21233/) (2026)

## Abstract (arXiv)

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.