When More Candidates Hurt: A Proposal–Evidence Reliability Frontier for Causal Selection

Candidate generation can scale faster than causal evaluation. Under a fixed evidence pool, enlarging a candidate prefix changes both the best attainable action and the statistical burden of comparing candidates with uneven causal support. We characterize this joint movement through a *proposal–evidence reliability frontier*. Proposal-Certified Selection (PCS) uses proposal-side predictions to order candidates and lower endpoints of simultaneous causal intervals to select among them. Its all-prefix certificate balances comparator miss against certification width across all covered prefixes. In a Gaussian benchmark, a lower bound yields a $\sqrt{\log K/n}$ comparison requirement, showing that logarithmic multiplicity persists beyond the plug-in selector. A proposal/support-aware allocation uniquely minimizes the stated expected certificate-width objective; realized decision performance is evaluated separately. Controlled diagnostics reproduce the predicted selection-error and multiplicity patterns. In Upworthy replays, the paired PCS gain over tuned fixed depth rises from 0.042 to 0.949 basis points as evidence increases from 0.5% to 5%. In a 26.9M-impression recommendation-log study, the randomized-audit click-through-rate (CTR) gain over inverse-propensity-score (IPS) plug-in is 26.70 basis points (95% cluster interval $[10.99,42.42]$). The ten-issue persuasion study remains unresolved, and uniform allocation nearly matches adaptive allocation in recommendation. Together, these results separate proposal coverage from statistically usable decision improvement.