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.

翻译：现有数字广告中的自动出价算法通常将广告机会的价值视为广告展示和/或点击时获得的收益，并据此出价。这种策略可能导致浪费性支出，因为真正的价值在于付费曝光带来的边际收益：即使未赢得赞助广告位，广告主仍可能通过自然搜索结果（例如在谷歌或亚马逊上）获得收益。受近期研究启发，我们将广告价值建模为处理效应——即赢得与输掉拍卖之间的结果差异——并在此因果视角下研究第二价格（维克里）拍卖中的在线出价学习。我们开发了在多种反馈模型下达到速率最优遗憾的算法。其中一项关键要素利用了第二价格支付规则所揭示的信息，这相较于第一价格拍卖中的类似学习问题，严格改进了遗憾表现。