Online Causal Inference for Advertising in Real-Time Bidding Auctions
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 recov-ered, we introduce an adapted Thompson sampling 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.