Counterfactual Data Augmentation for Artificial Intelligence Agriculture Yield Prediction Under Confounded Rainfall: A Computer Off-Policy Generalization Bound

Crop yield prediction models trained on observational data inherit the confounding structure of the rainfall regimes under which they were estimated, and this inheritance is not corrected by adding more data from the same regime. The central result is a generalization bound that decomposes the target risk into three terms: an importance-weighted empirical risk, a Rademacher complexity term scaled by the density ratio, and an irreducible bias term that measures the divergence between the behaviour and target rainfall distributions. The bound is stated for a bounded yield loss and a bounded density ratio, and it is shown to be tight in the sense that the bias term cannot be removed by any augmentation procedure that draws only from the behaviour distribution. A counterfactual augmentation operator is then defined as a transport map from the behaviour rainfall distribution to a target rainfall distribution, and the bound is extended to cover augmentation error. The analysis is illustrated with a reanalysis temperature series for Nairobi, Kenya (NASA POWER MERRA-2, 2000-2023, 312 monthly observations, mean 20.146 C, warming trend 0.2477 C per decade), which is used only to motivate the magnitude of distribution shift that the bound must accommodate, not as a yield dataset. Augmentation is best understood as a variance-reduction device whose benefit is bounded by the quality of the transport map, and that reporting a single held-out error for a yield model trained under confounded rainfall is not a valid estimate of target-regime performance.