**StatOrch: Evidence-Conditioned Stagewise Inference Under Unknown Distribution Shift**

Inference on an unlabeled target population requires deciding how to respond to distribution shift: correct class proportions, adapt a predictor, acquire measurements, or retain the current procedure. We present StatOrch, a framework connecting observable statistical evidence to these choices through stage-specific effect prediction and fixed fallbacks. It organizes established tools around population targets, dependent contexts, and resource constraints. Controlled evaluations show routing gains over validation-selected fixed actions. An executed adaptation–quantification–classification chain improves the joint population-and-prediction objective on seen mechanisms across four fitting seeds on a fixed cohort, with no corresponding advantage established on unseen mechanisms. A matched one-shot complete-pipeline selector attains similar losses; these results do not establish a stagewise-selection advantage. Separately, an archival measurement/resource instance with fixed downstream heads records 21.1% lower known-class conditional prevalence error, 5.26 percentage points higher Macro-F1, and 10.5% lower recorded measurement cost than a near-cost fixed policy on 1,232 held-out units, under its preserved ledger. Acquisition, re-entry, and real-sensor stress tests characterize operational behavior under their respective protocols. The controlled results support conditional selection and dependent execution within the evaluated settings.