When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits
Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning. Teams therefore train the bandit on a fast proxy reward, and separately must judge whether a contextual bandit is worth its complexity over sending one best message. Settling both decisions with the usual offline checks - a batch off-policy estimate, a margina
Record details
Published: 12 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Regulation · Healthcare
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits,” evidence record 19070, https://ethics.ai/record/19070 (originally published by arXiv cs.LG).
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