Counterfactual Data Augmentation for Artificial Intelligence Agriculture Crop Yield Prediction Under Unobserved Management Practices: A Computer Causal-Identification Evaluation

Crop yield prediction models trained on observational farm data routinely condition on management practices that are recorded inconsistently or not at all, so that predictions for counterfactual practice combinations rest on extrapolation rather than identification. the present analysis formalises the conditions under which counterfactual data augmentation can recover a management-conditional yield response from observational data with unobserved confounding, and evaluates the operating characteristics of a before-after-control-impact (BACIP) estimator under a declared variance structure. The analysis is a design-based power study, not an empirical application: no observational data are used or claimed. Under a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5 and an intraclass correlation of 0.64, power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Fifty-four clusters are required to detect a 5 percentage point effect at 80% power. Augmentation cannot substitute for identification: it can only reweight or impute within the support of the observed management distribution, and its validity depends on an exchangeability assumption that the BACIP design makes explicit and testable. The contribution is a formal statement of the identification boundary of counterfactual augmentation for agricultural yield prediction, together with the design parameters that make the boundary empirically consequential.