Online Causal Inference for Advertising in Real-Time Bidding Auctions
This work addresses the problem of efficient and cost-effective advertising evaluation for advertisers in digital marketing, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of assessing advertising effectiveness in real-time bidding auctions by proposing a causal inference approach that identifies effects through optimal bids, using an adapted Thompson sampling algorithm to recover these bids with an order-optimal regret bound and outperforming common methods in data experiments.
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.