Work

Flow of dynamical causal structures with an application to correlations

Year: 2025

Type: article

Abstract: Causal models capture cause-effect relations both qualitatively—via the graphical —and quantitatively—via the . They offer a powerful framework for analyzing and constructing processes. Here, we introduce a tool—the flow of causal structures—to visualize and explore the of classical-deterministic processes, arguably like those present in general relativity. The flow describes all possible ways in which the causal structure of a process can evolve. We also present an algorithm to construct its supergraph—the —from the , without invoking the model parameters. As an application, we show that if all leaves of a flow are trivial, then the corresponding process produces causal correlations only, i.e., correlations where future data cannot influence past events. This strengthens the result that processes, where every directed cycle in their causal structure is chordless, establish causal correlations only. We also discuss the main difficulties for the quantum generalization of the present algorithms.

Source: Physical Review Research

DOI: 10.1103/jsmm-tq72

Authors: Ämin Baumeler, Stefan Wolf

Institution: Università della Svizzera italiana

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Hasler Stiftung