mlsanityData to 14 Oct 2026

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

arXiv 2605.01756 ↗First posted 3 May 2026cs.GT cs.IT cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 37 references that are in the map. First posted on arXiv 3 May 2026.

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13 of 37 references.

References · 13

  1. 2015Online learning in repeated auctionscited by 39
  2. 2017Learning to Bid Without Knowing your Valuecited by 34
  3. 2020Optimal No-regret Learning in Repeated First-price Auctionscited by 33
  4. 2020Learning to Bid Optimally and Efficiently in Adversarial First-price Auctionscited by 33
  5. 2021Learning to Bid in Contextual First Price Auctionscited by 19
  6. 2023The Role of Transparency in Repeated First-Price Auctions with Unknown Valuationscited by 15
  7. 2015Lift-Based Bidding in Ad Selectioncited by 8
  8. 2019Online Causal Inference for Advertising in Real-Time Bidding Auctionscited by 6
  9. 2025Joint Value Estimation and Bidding in Repeated First-Price Auctionscited by 6
  10. 2022Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewardscited by 5
  11. 2024Stochastic contextual bandits with graph feedback: from independence number to MAS numbercited by 5
  12. 2022Incrementality Bidding and Attributioncited by 3
1 more
  1. 2025Optimal Arm Elimination Algorithms for Combinatorial Banditscited by 3

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