# Joint Value Estimation and Bidding in Repeated First-Price Auctions

arXiv: https://arxiv.org/abs/2502.17292 · HTML: https://mlsanity.com/paper/2502.17292/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Sequential Batch Learning in Finite-Action Linear Contextual Bandits](https://mlsanity.com/paper/2004.06321/) (2020)
- [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)
- [Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality](https://mlsanity.com/paper/2212.09900/) (2022)
- [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)
- [Contextual Bandits with Cross-learning](https://mlsanity.com/paper/1809.09582/) (2018)
- [Auto-bidding and Auctions in Online Advertising: A Survey](https://mlsanity.com/paper/2408.07685/) (2024)
- [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)
- [Learning to Bid in Repeated First-Price Auctions with Budgets](https://mlsanity.com/paper/2304.13477/) (2023)
- [No-Regret Algorithms in non-Truthful Auctions with Budget and ROI Constraints](https://mlsanity.com/paper/2404.09832/) (2024)
- [Fast Rate Learning in Stochastic First Price Bidding](https://mlsanity.com/paper/2107.01835/) (2021)
- [Bayesian Nash Equilibrium in First-Price Auction with Discrete Value Distributions](https://mlsanity.com/paper/1906.09403/) (2019)
- [Online Causal Inference for Advertising in Real-Time Bidding Auctions](https://mlsanity.com/paper/1908.08600/) (2019)
- [On the Complexity of Equilibrium Computation in First-Price Auctions](https://mlsanity.com/paper/2103.03238/) (2021)
- [Computing Bayes Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging](https://mlsanity.com/paper/2208.02036/) (2022)
- [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023)
- [Complexity of Equilibria in First-Price Auctions under General Tie-Breaking Rules](https://mlsanity.com/paper/2303.16388/) (2023)
- [Causal Effect Estimation after Propensity Score Trimming with Continuous Treatments](https://mlsanity.com/paper/2309.00706/) (2023)
- [On the Computation of Equilibria in Discrete First-Price Auctions](https://mlsanity.com/paper/2402.12068/) (2024)

## Built on by (most cited first, 6 of 6)

- [Optimal Arm Elimination Algorithms for Combinatorial Bandits](https://mlsanity.com/paper/2510.23992/) (2025)
- [The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions](https://mlsanity.com/paper/2605.01756/) (2026)
- [Multi-User Contextual Cascading Bandits for Personalized Recommendation](https://mlsanity.com/paper/2508.13981/) (2025)
- [Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions](https://mlsanity.com/paper/2605.09448/) (2026)
- [Nearly Optimal Best Arm Identification for Semiparametric Bandits](https://mlsanity.com/paper/2604.03969/) (2026)
- [Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing](https://mlsanity.com/paper/2608.12831/) (2026)

## Abstract (arXiv)

We study regret minimization in repeated first-price auctions (FPAs), where a bidder observes only the realized outcome after each auction -- win or loss. This setup reflects practical scenarios in online display advertising where the actual value of an impression depends on the difference between two potential outcomes, such as clicks or conversion rates, when the auction is won versus lost. We incorporate causal inference into this framework and analyze the challenging case where only the treatment effect admits a simple dependence on observable features. Under this framework, we propose algorithms that jointly estimate private values and optimize bidding strategies under two different feedback types on the highest other bid (HOB): the full-information feedback where the HOB is always revealed, and the binary feedback where the bidder only observes the win-loss indicator. Under both cases, our algorithms are shown to achieve near-optimal regret bounds. Notably, our framework enjoys a unique feature that the treatments are actively chosen, and hence eliminates the need for the overlap condition commonly required in causal inference.