An Analysis of Selection Bias Issue for Online Advertising
Not on the map. Why?
Abstract (arXiv)
In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that is present in an auction system. We analyze that the selection bias destroy truthfulness of the auction, which implies that the buyers (advertisers) on the auction can not maximize their profits. Although selection bias is well known in the field of statistics and there are lot of studies for it, our main contribution is to combine the theoretical analysis of the bias with the auction mechanism. In our experiment using online A/B testing, we evaluate the selection bias on an auction system whose ranking score is the function of predicted CTR (click through rate) of advertisement. The experiment showed that the selection bias is drastically reduced by using a multi-task learning which learns the data for all advertisements.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 7 Jun 2022.
Builds on
10 of 34 references.
References · 10
- 2013Exact post-selection inference, with application to the lassocited by 91
- 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
- 2017Optimized Cost per Click in Taobao Display Advertisingcited by 57
- 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
- 2017Why Adaptively Collected Data Have Negative Bias and How to Correct for Itcited by 36
- 2014Exact Post Model Selection Inference for Marginal Screeningcited by 12
- 2017Profit Maximization for Online Advertising Demand-Side Platformscited by 9
- 2017Robust Budget Allocation via Continuous Submodular Functionscited by 8
- 2016Cost-sensitive Learning for Utility Optimization in Online Advertising Auctionscited by 7
- 2019Online Learning for Measuring Incentive Compatibility in Ad Auctionscited by 5
Built on by
Not cited yet.