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

arXiv: https://arxiv.org/abs/2605.01756 · HTML: https://mlsanity.com/paper/2605.01756/

## 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.

## Builds on (most cited first, 13 of 13)

- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [Learning to Bid Without Knowing your Value](https://mlsanity.com/paper/1711.01333/) (2017)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](https://mlsanity.com/paper/2007.04568/) (2020)
- [Learning to Bid in Contextual First Price Auctions](https://mlsanity.com/paper/2109.03173/) (2021)
- [The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations](https://mlsanity.com/paper/2307.09478/) (2023)
- [Lift-Based Bidding in Ad Selection](https://mlsanity.com/paper/1507.04811/) (2015)
- [Online Causal Inference for Advertising in Real-Time Bidding Auctions](https://mlsanity.com/paper/1908.08600/) (2019)
- [Joint Value Estimation and Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2502.17292/) (2025)
- [Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards](https://mlsanity.com/paper/2206.01293/) (2022)
- [Stochastic contextual bandits with graph feedback: from independence number to MAS number](https://mlsanity.com/paper/2402.18591/) (2024)
- [Incrementality Bidding and Attribution](https://mlsanity.com/paper/2208.12809/) (2022)
- [Optimal Arm Elimination Algorithms for Combinatorial Bandits](https://mlsanity.com/paper/2510.23992/) (2025)

## Built on by (most cited first, 2 of 2)

- [Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions](https://mlsanity.com/paper/2605.09448/) (2026)
- [Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing](https://mlsanity.com/paper/2608.12831/) (2026)

## 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.