# Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

arXiv: https://arxiv.org/abs/2306.03874 · HTML: https://mlsanity.com/paper/2306.03874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [A System for Explainable Answer Set Programming](https://mlsanity.com/paper/2009.10242/) (2020)
- [The Counterfactual NESS Definition of Causation](https://mlsanity.com/paper/2012.05123/) (2020)
- [Causes and Explanations: A Structural-Model Approach --- Part 1: Causes](https://mlsanity.com/paper/1301.2275/) (2013)
- [Enablers and Inhibitors in Causal Justifications of Logic Programs](https://mlsanity.com/paper/1602.06897/) (2016)

## Abstract (arXiv)

This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.