5 papers
Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
Prashant Shekhar, Caroline Howard
We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental respo…
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Prashant Shekhar, Caroline Howard
Advertising platforms use randomized lift tests to measure incrementality, but privacy-preserving reporting systems degrade the observed signal through match-rate loss, linkability…
Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
Prashant Shekhar, Caroline Howard
Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through bu…
Support-aware offline policy selection for advertising marketplaces
Prashant Shekhar, Caroline Howard
Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield gains, yet they can also hide…
Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness
Prashant Shekhar, Caroline Howard
Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not imply that a policy is safe to dep…