Causal Inference for Artificial Intelligence Agriculture Advisories Under Peer Contagion in Farmer Networks: A Computer Network-Interference Identification Evaluation

Artificial intelligence advisories in agriculture are typically evaluated as if each farmer's outcome responds only to their own treatment assignment, yet advisory information travels through farmer networks and contaminates control farmers. the present analysis formalises the resulting identification problem as a network-interference causal model and evaluates, through a replication-based design study, whether the before-after-control-impact (BACI) estimator recovers the direct effect of an advisory under peer contagion. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster SD = 6.0, residual SD = 4.5, and intraclass correlation = 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Power curves, minimum detectable effect curves, coefficient plots, density estimates, a scatter fit and residual diagnostics were produced. The design required 52 clusters to detect a 5 percentage point effect at 80% power. BACI identifies a composite of direct and spillover effects rather than the direct effect alone, and that network-interference identification requires either cluster-level randomisation with saturation or explicit partial-interference modelling. The contribution is a formal statement of the bias and a design-theoretic account of when BACI remains defensible for AI advisory evaluation.