The Winner's Curse in Data-Driven Decisions
from Best AI papers explained Hosted by Enoch H. Kang
This academic paper addresses the "winner's curse" in data-driven decision-making, a phenomenon where selecting optimal policies based on estimated effects leads to overly optimistic evaluations of actual policy value. The authors theoretically demonstrate the existence of this curse and empirically illustrate its presence across various marketing applications like A/B testing and personalized targeting. To mitigate this pervasive problem, they propose a novel correction method utilizing a non-continuous bootstrap approach, which consistently performs well and often outperforms existing context-specific solutions. Through extensive simulations, the paper validates the effectiveness of their bootstrap-based correction across different scenarios, including those with multiple segments and budget constraints, even in cases of misspecified demand models.
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