Causal Invariant Bio-Inference: Breaking the Observational Barrier in Rhizosphere Dynamics via Physically-Constrained Edge-GNN Structural Causal Models

Abstract

Observational machine learning models (e.g., Transformers, CNNs, Random Forests) fit joint probability distributions P(Y|X) and inherently break down out-of-distribution when spurious correlations fail under environmental volatility. In rhizosphere dynamics, unobserved confounders and micro-climatic shifts frequently lead to catastrophic prediction failures. To break this observational barrier, we introduce Causal Invariant Bio-Inference for RADIUS 0 / MediEEZI, combining Judea Pearl’s do-calculus interventional framework P(Y|do(X=x*)) with structural causal models (SCMs) and decentralized Edge Graph Neural Networks (Edge-GNNs). By embedding a stoichiometric and biochemical constraint matrix (C_bio derived from KEGG/MetaCyc pathways) directly into the adjacency optimization, we restrict the causal search space to thermodynamically allowed interactions and enforce strict directed acyclic graph (DAG) topology via matrix exponential constraints. This physically-constrained architecture enables provably convergent, continuous in-situ causal bio-inference across soil-plant-microbiome interactions, closed-loop environmental control life support systems (ECLS), and non-invasive health monitoring without requiring destructive sampling.