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.
Proceedings of the 56th Annual ACM Symposium on Theory of Computing , pages=
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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.