The Winner's Curse in Data-Driven Decisions
from Best AI papers explained Hosted by Enoch H. Kang
The document outlines how data-driven decision-making, particularly in marketing, is susceptible to the "winner's curse," a phenomenon where selected optimal policies are overvalued due to estimation errors. It explains that this upward bias occurs because algorithms tend to pick options that appear best in available data, even if their true performance is lower. The authors demonstrate this curse theoretically and through simulations in various marketing contexts, such as A/B testing and personalized targeting. To mitigate this over-optimism, the paper proposes a robust bootstrap-based correction method, which consistently outperforms or complements existing solutions like sample splitting, Bayesian shrinkage, and selective inference, despite some limitations with non-smooth functions.
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