Thorsten Franke

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

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 l...