Information-theoretic signatures of causality in Bayesian networks and hypergraphs

This article likely presents research on identifying causal relationships within complex systems using information theory. The focus is on Bayesian networks and hypergraphs, which are mathematical frameworks for representing probabilistic relationships and higher-order interactions, respectively. The use of information-theoretic measures suggests an approach that quantifies the information flow and dependencies to infer causality. The ArXiv source indicates this is a pre-print, meaning it's likely undergoing peer review or has not yet been formally published.

• •The research explores causal inference using information theory.
• •It focuses on Bayesian networks and hypergraphs.
• •The approach likely involves quantifying information flow to identify causal relationships.