Evaluation of Counterfactual Audit Sampling for Artificial Intelligence Agriculture Procurement Under Bid-Rigging: A Governance Detection-Power Bound for Tender Integrity

Artificial intelligence systems are increasingly procured by agricultural ministries to allocate subsidies, score grant applications and forecast harvests, yet the audit designs meant to detect bid-rigging in those procurements have not been evaluated for their detection power. the present analysis treats counterfactual audit sampling as a governance instrument and derives a detection-power bound for the before-after-control-impact design that underlies it. The analysis is a replication-based evaluation of the estimator's operating characteristics under a declared variance structure: between-cluster standard deviation of 6.0, residual standard deviation of 4.5 and intraclass correlation of 0.64. Power was estimated across 2000 design cells defined by cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design required 52 clusters to reach 80 per cent power at a 5 percentage point effect. At 8, 16, 24 and 32 clusters, power at that effect remained below the conventional threshold, and the minimum detectable effect at 80 per cent power fell only slowly as clusters were added. The bound implies that counterfactual audit sampling, as currently constituted, cannot certify tender integrity at the cluster counts African agricultural agencies can realistically audit. The contribution is a governance detection-power bound that reframes audit sampling as a statistical design problem rather than an administrative ritual.