# Causal Kripke Models

arXiv: https://arxiv.org/abs/2307.05631 · HTML: https://mlsanity.com/paper/2307.05631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility](https://mlsanity.com/paper/1810.05903/) (2018)
- [Causal Explanations and XAI](https://mlsanity.com/paper/2201.13169/) (2022)
- [From Verification to Causality-based Explications](https://mlsanity.com/paper/2105.09533/) (2021)
- [Blameworthiness in Multi-Agent Settings](https://mlsanity.com/paper/1903.04102/) (2019)
- [The Counterfactual NESS Definition of Causation](https://mlsanity.com/paper/2012.05123/) (2020)
- [Program Actions as Actual Causes: A Building Block for Accountability](https://mlsanity.com/paper/1505.01131/) (2015)
- [Thinking About Causation: A Causal Language with Epistemic Operators](https://mlsanity.com/paper/2010.16217/) (2020)
- [(De-)Composing Causality in Labeled Transition Systems](https://mlsanity.com/paper/1608.07880/) (2016)

## Abstract (arXiv)

This work extends Halpern and Pearl's causal models for actual causality to a possible world semantics environment. Using this framework we introduce a logic of actual causality with modal operators, which allows for reasoning about causality in scenarios involving multiple possibilities, temporality, knowledge and uncertainty. We illustrate this with a number of examples, and conclude by discussing some future directions for research.