Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental response, and member-experience load. Instead of calibrating a generic forecast residual, the framework measures forecast error by its largest impact on the policies that could actually be deployed. The main theorem shows that the proposed score is the smallest valid uncertainty measure that uniformly protects all deployable pacing policies. Geometrically, it is the support function of the signed policy sensitivity set. Split conformal calibration gives finite-sample coverage for this score. A high-dimensional separa...
| Authors | Prashant Shekhar, Caroline Howard |
|---|---|
| Primary category | stat.ML |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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