# Bell's local causality is a d-separation criterion

## TL;DR

This paper connects Bell's concept of local causality with Bayesian network theory, showing that the regions shielding off correlations correspond to d-separating sets, providing a formal criterion for local causality.

## Contribution

It demonstrates that Bell's local causality can be understood as a d-separation criterion within Bayesian networks, linking quantum causality notions with probabilistic graphical models.

## Key findings

- Shielder-off regions align with d-separating sets in Bayesian networks.
- Bell's local causality can be formalized using d-separation.
- Provides a probabilistic graphical framework for local causality.

## Abstract

This paper aims to motivate Bell's notion of local causality by means of Bayesian networks. In a locally causal theory any superluminal correlation should be screened off by atomic events localized in any so-called \textit{shielder-off region} in the past of one of the correlating events. In a Bayesian network any correlation between non-descendant random variables are screened off by any so-called \textit{d-separating set} of variables. We will argue that the shielder-off regions in the definition of local causality conform in a well defined sense to the d-separating sets in Bayesian networks.