Abstract The stability of critical agricultural supply chains is fundamental to global food security, yet their inherent complexity challenges traditional risk management approaches. This paper introduces and validates a data-driven causal network framework, employing the Peter and Clark Momentary Conditional Independence (PCMCI) algorithm, to systematically disentangle these complex dynamics.