Artificial Intelligence Causal Inference of Community Clinic Antibiotic Stewardship Guidance Under Unmeasured Prescriber Habituation: A Targeted Maximum Likelihood Healthcare Resistance-Externality Evaluation

Community clinic antibiotic stewardship programmes are routinely evaluated with before-after designs that compare prescribing volumes across calendar periods, yet prescriber habituation to guidance is unmeasured and confounds the very contrast such designs rely on. Because no observational data are available, the study characterises the operating characteristics of the estimator under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 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.54 clusters are required to detect a 5 percentage point effect at 80 per cent power, that the minimum detectable effect falls monotonically with cluster count, and that the TMLE-BACIP estimator retains nominal coverage when habituation is misspecified as a fixed effect. The contribution is a design-theoretic account of when stewardship guidance can be causally identified under unmeasured habituation, together with the sample-size thresholds that make such identification credible in community clinic settings.