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Online Causal Inference for Advertising in Real-Time Bidding Auctions
Real-time bidding (RTB) systems, which leverage auctions to programmatically
allocate user impressions to multiple competing advertisers, continue to enjoy
widespread success in digital advertising. Assessing the effectiveness of such
advertising remains a lingering challenge in research and practice. This paper
presents a new experimental design to perform causal inference on advertising
bought through such mechanisms. Our method leverages the economic structure of
first- and second-price auctions, which are ubiquitous in RTB systems, embedded
within a multi-armed bandit (MAB) setup for online adaptive experimentation. We
implement it via a modified Thompson sampling (TS) algorithm that estimates
causal effects of advertising while minimizing the costs of experimentation to
the advertiser by simultaneously learning the optimal bidding policy that
maximizes her expected payoffs from auction participation. Simulations show
that not only the proposed method successfully accomplishes the advertiser's
goals, but also does so at a much lower cost than more conventional
experimentation policies aimed at performing causal inference
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