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

2026-05-03

AI Summary

This work addresses a critical limitation in existing automated bidding algorithms, which often conflate ad impression or click revenue with true advertiser value and neglect the counterfactual benefit of organic search traffic when auctions are lost, leading to inefficient budget allocation. The paper introduces a novel approach that models ad value as the marginal causal effect between winning and losing an auction. By leveraging payment information revealed through the second-price auction mechanism, the authors design an online bidding strategy that achieves theoretically optimal regret bounds across multiple feedback models. This method substantially outperforms counterparts in first-price auction settings and significantly enhances both advertising efficiency and return on investment.

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.

Problem

• advertising
• auto-bidding
• marginal value
• causal inference
• second-price auctions

Innovation

• causal inference
• second-price auction
• online learning
• auto-bidding
• marginal value