Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
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A Bayesian quantile regression framework for misclassified binary outcomes is proposed and applied to spousal violence data, revealing higher underreporting than overreporting and altered conclusions about associations with employment and wealth.
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The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
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Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression
A Bayesian quantile regression framework for misclassified binary outcomes is proposed and applied to spousal violence data, revealing higher underreporting than overreporting and altered conclusions about associations with employment and wealth.