Artificial Intelligence Causal Inference of Community Clinic Preeclampsia Aspirin Prophylaxis Uptake Under Unmeasured Gestational Age Dating Error: A Healthcare Instrumental Variable Equity Evaluation

Gestational age dating error is a structural threat to the causal evaluation of preeclampsia aspirin prophylaxis in community clinics: the same ultrasound dating imprecision that determines eligibility also determines who is offered prophylaxis, so naive comparisons of uptake between early and late bookers confound treatment assignment with the exposure that defines the indication. The analysis is a replication-based evaluation of the before-after-control-impact estimator, not an observational study: no patient 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 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Fifty-four clusters were required to detect a 5 percentage point effect at 80 per cent power. The framework proposes how instrumental variable inference can separate dating-error confounding from prophylaxis uptake, and it specifies the equity conditions under which the resulting estimates are interpretable rather than artefactual.