DRIFT: Causal Inference for Generated Treatments When Replay Is Not Provenance

Causal effects of generated treatments depend not only on which candidates were shown, but also on the historical mechanism that selected among them. When that mechanism is no longer reproducible, current replay estimates today's selector rather than reconstructing yesterday's assignment law. We prove that even unlimited replay and exact aggregate calibration can leave distinct historical causal effects observationally indistinguishable. This provenance boundary motivates Drift-Robust Inference From Treatment traces (DRIFT), which turns a prespecified or independently audited historical-to-current log-odds bridge into inverse-selection envelopes, observable calibration restrictions, and dual causal certificates. The resulting interval is sharp for the specified marginal trace-weight model and outer-valid for every compatible latent historical effect; at compatible zero drift it reduces to the ordered binary-pair logged counterfactual exposure functional. With bounded held-out dual scores and independent population-valid replay bands, cross-fitting yields finite-sample outer confidence sets even under approximate optimization. Regular endpoints separate historical and replay uncertainty, whereas nonregular endpoints need not admit Gaussian inference; on an explicit compact class, the minimax expected squared Hausdorff risk is of order $n^{-1}+B^{-1}$. The same dual geometry quantifies where historical audits can most reduce causal ambiguity and yields a first-order cost-aware allocation rule. A controlled HelpSteer2 study tests certificates with real ratings, constructed selectors, and potential-rating coupling; predicted inclusions are observed in valid conditions. A fixed-action real-log diagnostic shows operation under injected mismatch without validating causal coverage. Eight synthetic studies probe identification, inference, and audits: replay estimates today's selector but need not recover the historical selection law.