2019/08/22 by Caio Waisman, Waisman, Caio, Harikesh S. Nair +4 · 1 voice · 2 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Data Stream Mining Techniques #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.GT #cs.LG #econ.EM #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.08600
openalex publication_date 2019/08/22 · arxiv published 2019/08/22 · arxiv updated 2024/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on advertising bought through such mechanisms. Leveraging the economic structure of first- and second-price auctions, we first show that the effects of advertising are identified by the optimal bids. Hence, since these optimal bids are the only objects that need to be recovered, we introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem that succeeds in recovering such bids and, consequently, the effects of advertising while minimizing the costs of experimentation. We derive a regret bound for our algorithm which is order optimal and use data from RTB auctions to show that it outperforms commonly used methods that estimate the effects of advertising.