Counterfactual Data Augmentation for Artificial Intelligence Agriculture Pest Forecasting Under Unobserved Microclimate Confounding: A Computer Off-Policy Risk Bound

Pest forecasting models in agriculture are trained on observational data in which microclimate variables that drive both pest dynamics and farmer interventions are rarely recorded. When such unobserved confounding is present, standard supervised learning yields predictors whose risk on the deployment policy diverges from the risk estimated on the training distribution, and the divergence is not detectable from held-out validation alone. The contribution is a counterfactual data-augmentation scheme coupled with an off-policy risk bound that is computable from logged data and a bounded sensitivity parameter on the unobserved microclimate. The bound is derived from a change-of-measure argument combined with a sensitivity model for the unobserved confounder, and it is shown to be tight in the no-confounding limit and to degrade gracefully as the sensitivity parameter grows. The derivation is illustrated with a reanalysis of monthly air temperature at 2 m for Accra, Ghana, from the NASA POWER MERRA-2 record for 2000 to 2023 (312 monthly observations, mean 26.521 C, warming trend 0.1622 C per decade), which supplies the climatological scale on which the sensitivity parameter is calibrated. The bound provides a defensible substitute for the held-out validation that is unavailable when the deployment policy differs from the logging policy, and it identifies the conditions under which the augmentation scheme is preferable to importance-weighted alternatives.