# How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming

arXiv: https://arxiv.org/abs/2607.21208 · HTML: https://mlsanity.com/paper/2607.21208/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [Computing LPMLN Using ASP and MLN Solvers](https://mlsanity.com/paper/1707.06325/) (2017)
- [How Artificial Intelligence Leads to Knowledge Why: An Inquiry Inspired by Aristotle's Posterior Analytics](https://mlsanity.com/paper/2504.02430/) (2025)
- [How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs](https://mlsanity.com/paper/2507.05088/) (2025)
- [Weighted Rules under the Stable Model Semantics](https://mlsanity.com/paper/2605.09519/) (2026)

## Abstract (arXiv)

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.