Causal graph discovery for disruptions

A deployable approach must explain what it optimizes, what it leaves uncertain and when it should defer. Those questions guide this brief on causal graph discovery for disruptions.

Practitioner lens

The strongest evaluation of causal graph discovery for disruptions compares decision quality with a transparent baseline, preserves time ordering and reports how performance varies by scenario.

First experiment

Write down the objective, constraints, uncertainty set and decision horizon before selecting a solver or model.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.