Causality as an emergent property of biochemical learning: A theory of pre-cognitive inference

We argue that causality is not fundamental but an emergent constraint learned by open biochemical systems as they compress structured environmental regularities. Building on neural-physics and renormalization ideas, we formalize a multiscale learning flow in which biochemical networks reorganize to reduce dissipation and prediction error. From stochastic microdynamics (Langevin, Fokker–Planck, path integrals) we describe how mesoscale causal organization —characterized by modularity, locality, and intervention-stability—can arise as fixed points of a constraint-learning flow 𝜷 ⁡ ( 𝝓 ) over a finite set of structural order parameters 𝝓 . We propose operational markers (transfer entropy, integrated information decomposition 𝛷 I D , graph entropies) and falsifiable experiments in Physarum , fungal electronics, and gene-regulatory networks that test for entropy collapse, gradient-reversal adaptation, and anticipatory dynamics. Unlike accounts that assume causal structure, we specify when and how it can form . This reframes causality as the macroscopic residue of adaptive coarse-graining rather than a primitive law, linking origins-of-life research, minimal cognition, and causal representation learning. This is a theoretical contribution: it reports no new experimental data, but it states a concrete minimal model and a falsifiable experimental program.