Causality and the Semantics of Provenance

TL;DR

This paper explores the formal relationship between causality and provenance, proposing a mathematical framework based on structural models to better understand and justify provenance mechanisms.

Contribution

It introduces a formal approach using structural causal models to analyze and interpret provenance graphs, bridging causality theory and data provenance.

Findings

Causality models can clarify provenance semantics.

Formal causality frameworks help evaluate provenance mechanisms.

Work in progress on applying causality to provenance graphs.

Abstract

Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web. Many provenance mechanisms have been developed, motivated by informal notions such as influence, dependence, explanation and causality. However, there has been little study of whether these mechanisms formally satisfy appropriate policies or even how to formalize relevant motivating concepts such as causality. We contend that mathematical models of these concepts are needed to justify and compare provenance techniques. In this paper we review a theory of causality based on structural models that has been developed in artificial intelligence, and describe work in progress on using causality to give a semantics to provenance graphs.