# A Real-World Implementation of Unbiased Lift-based Bidding System

arXiv: https://arxiv.org/abs/2202.13868 · HTML: https://mlsanity.com/paper/2202.13868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback](https://mlsanity.com/paper/1909.03601/) (2019)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Pacing Equilibrium in First-Price Auction Markets](https://mlsanity.com/paper/1811.07166/) (2018)
- [Lift-Based Bidding in Ad Selection](https://mlsanity.com/paper/1507.04811/) (2015)
- [Bid Shading by Win-Rate Estimation and Surplus Maximization](https://mlsanity.com/paper/2009.09259/) (2020)
- [Fatigue-Aware Ad Creative Selection](https://mlsanity.com/paper/1908.08936/) (2019)
- [Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2007.04002/) (2020)

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

- [Maximizing the Success Probability of Policy Allocations in Online Systems](https://mlsanity.com/paper/2312.16267/) (2023)
- [Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens](https://mlsanity.com/paper/2510.23410/) (2025)

## Abstract (arXiv)

In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding.Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves maximization of target metrics such as sales. Despiteits superiority, lift-based bidding has not yet been widely accepted in the advertising industry. For one reason, lift-based bidding is less profitable for DSP providers under the current billing rule. Second, thepractical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact.We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules. We conduct extensive experiments usinga real-world advertising campaign and examine the performance under various settings. We find that lift-based bidding, especially unbiased lift-based bidding is most profitable for both DSP providers and advertisers. Our ablation study highlights that lift-based bidding has a good property for currently dominant first price auctions. The results will motivate the online