The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
For digital advertisers and ad platforms, this provides a theoretically grounded framework to avoid overpaying for ads that would have been seen organically, with concrete regret guarantees.
The paper addresses the problem of wasteful ad spending by modeling ad value as a causal treatment effect (the marginal gain from winning vs. losing an auction) and develops online learning algorithms for bidding in second-price auctions that achieve rate-optimal regret, exploiting the second-price payment rule to improve regret over first-price auctions.
Existing auto-bidding algorithms in digital advertising often treat the value of an ad opportunity as the revenue obtained when an ad is shown and/or clicked, and bid accordingly. This can lead to wasteful spending because the true value is the marginal gain from paid exposure: even without winning a sponsored slot, an advertiser may still earn revenue via an organic search result (e.g., on Google or Amazon). Motivated by recent work, we model ad value as a treatment effect--the outcome difference between winning and losing the auction--and study online learning for bidding in second-price (Vickrey) auctions under this causal perspective. We develop algorithms that attain rate-optimal regret under several feedback models. A key ingredient exploits the information revealed by the second-price payment rule, which strictly improves regret relative to analogous learning problems in first-price auctions.