Work

Causality as a Minimum Energy Principle

Year: 2026

Type: preprint

Abstract: Classical causal models, such as Granger causality and structural equation modeling, are largely restricted to acyclic interactions and struggle to represent cyclic and higher-order dynamics in complex networks. We introduce a causal framework grounded in a variational principle, interpreting causality as directional energy flow from high- to low-energy states along network connections. Using Hodge theory, network flows are decomposed into dissipative components and a persistent harmonic component that captures stable cyclic interactions. Applied to resting-state fMRI connectivity, our variational framework reveals robust cyclic causal patterns that are not detected by conventional causal models, highlighting the value of variational principles for causality.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.17151

Authors: Chung, Moo K., Anand, D. Vijay, El-Yaagoubi, Anass B, Jung, Jae-Hun, Qiu, Anqi, Ombao, Hernando

Institutions: University of Wisconsin–Madison, University College London, Pohang University of Science and Technology, Hong Kong Polytechnic University, King Abdullah University of Science and Technology

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, National Institute of Mental Health

Awards: Novel Continuous Structural and Functional Networks and Prediction of Individual Cognition, Dynamic embedding time series models in functional brain imaging

OpenAlex ID: W7155054652

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