Causal-Identification Bounds for Artificial Intelligence Agriculture Irrigation Inference Under Unmeasured Groundwater Pumping: A Computer Instrumental-Variable Sensitivity Evaluation

Artificial-intelligence irrigation advisories are typically evaluated with observational data in which groundwater pumping is unmeasured, so that any estimated effect of an advisory on water use conflates the advisory with the pumping behaviour that both determines and responds to it. the present analysis treats the resulting identification problem as a formal object and derives partial-identification bounds for the advisory effect under an instrumental-variable sensitivity model in which the exclusion restriction is allowed to fail by a bounded amount. The analysis is methodological and computational: no observational data are used or claimed. Instead, the operating characteristics of a before-after-control-impact design are characterised under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, 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. The bounds are shown to be sharp under stated assumptions, to collapse to the point-identified instrumental-variable estimand when the exclusion violation is zero, and to widen monotonically in the violation magnitude. Power analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80 per cent power under the declared variance structure. The contribution is a bound-based reporting standard that replaces the false precision of a single advisory-effect estimate with an interval whose width is an explicit function of the unmeasured-pumping assumption.