The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions

• URL: http://arxiv.org/abs/2605.01756v1
• Date: Sun, 03 May 2026 07:34:30 GMT
• Title: The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
• Abstract summary: We develop algorithms that attain rate-optimal regret under several feedback models.<n>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.
• Score: 34.5305475462577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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