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_ID, 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.