Counterfactual evaluation of AI policies

Model complexity should earn its place through better decisions, not novelty. This article explores counterfactual evaluation of ai policies and the baselines needed to establish its incremental value.

Practitioner lens

Stress-test counterfactual evaluation of ai policies beyond the average case. Tail events, correlated shortages and delayed information often determine whether a policy is robust enough to use.

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.