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Hamiltonian Causal Models (HCMs) introduce a trajectory-level framework that bridges statistical causal inference with non-equilibrium thermodynamics, enabling the detection of path-dependent effects invisible to standard average treatment analyses. By treating entropy production as a central observable, this approach allows for the separation of immutable equations of motion from intervenable mechanisms in dynamic physical systems. This work offers a novel theoretical foundation for modeling causality in non-stationary environments where traditional random variable relations are insufficient.
Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
arXiv:2606.04822v1 Announce Type: new Abstract: Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects, and feedback mediated by dynamics, all challenging in standard causal models. We introduce Hamiltonian Causal Models (HCMs), a trajectory-level framework in which observed variables interact with local environments and interventions act as controls of Hamiltonian mechanisms. HCMs separate immutable equations of motion from intervenable mechanisms and define causal effects as discrepancies between interventional path laws. A key motivation for HCMs is their natural interface with non-equilibrium thermodynamics. Entropy production quantifies the irreversibility of a process and is a central causal observable: it is estimable from data and witnesses causal effects along the system's evolution that are invisible to endpoint and cumulative versions of the standard average treatment effect. As in physics, cause and effect are not primitives of the relation between two random variables but arise from the non-invertibility of the thermodynamic arrow. With this, our paper reconciles the language of statistical causal models and non-stationary thermodynamics, offering new tools to describe causality in a wide range of physical systems.
Abstract:Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects, and feedback mediated by dynamics, all challenging in standard causal models. We introduce Hamiltonian Causal Models (HCMs), a trajectory-level framework in which observed variables interact with local environments and interventions act as controls of Hamiltonian mechanisms. HCMs separate immutable equations of motion from intervenable mechanisms and define causal effects as discrepancies between interventional path laws. A key motivation for HCMs is their natural interface with non-equilibrium thermodynamics. Entropy production quantifies the irreversibility of a process and is a central causal observable: it is estimable from data and witnesses causal effects along the system's evolution that are invisible to endpoint and cumulative versions of the standard average treatment effect. As in physics, cause and effect are not primitives of the relation between two random variables but arise from the non-invertibility of the thermodynamic arrow. With this, our paper reconciles the language of statistical causal models and non-stationary thermodynamics, offering new tools to describe causality in a wide range of physical systems.
Machine Learning (cs.LG)
arXiv:2606.04822 [cs.LG]
(or arXiv:2606.04822v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.04822 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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