# From probability to causality in probabilistic logic programming

arXiv: https://arxiv.org/abs/2608.07230 · HTML: https://mlsanity.com/paper/2608.07230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [Strong Completeness and Faithfulness in Bayesian Networks](https://mlsanity.com/paper/1302.4973/) (2013)
- [Structure Learning of Probabilistic Logic Programs by Searching the Clause Space](https://mlsanity.com/paper/1309.2080/) (2013)
- [On the Independencies Hidden in the Structure of a Probabilistic Logic Program](https://mlsanity.com/paper/2308.15865/) (2023)

## Abstract (arXiv)

Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system. When the structure of a probabilistic logic program is learned from data, however, only probabilistic information is used, and a single probability distribution may be compatible with several causal orders. This leads to ambiguity in interventional reasoning, raising the question of when the causal order is uniquely determined by the distribution. Exploiting the relationship between acyclic probabilistic logic programs and Bayesian networks, we derive conditions under which the probabilistic information encoded in a program determines a unique causal order. We also incorporate constraints arising from relational structure by taking into account prescribed sets of causal symmetries induced by the underlying relational vocabulary. The result is a method for verifying when a learned probabilistic logic program supports well-defined intervention semantics.