Advertisers that engage in real-time bidding (RTB) to display their ads commonly have two goals: learning their optimal bidding policy and estimating the expected effect of exposing users to their ads. Typical strategies to accomplish one of these goals tend to ignore the other, creating an apparent tension between the two . This paper exploits the economic structure of the bid optimization problem faced by advertisers to show that these two objectives can actually be perfectly aligned. By framing the advertiser's problem as a multi-armed bandit (MAB) problem, we propose a modified Thompson Sampling (TS) algorithm that concurrently learns the optimal bidding policy and estimates the expected effect of displaying the ad while minimizing economic losses from potential sub-optimal bidding . 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.
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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Advertisers that engage in real-time
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to display their ads commonly have two goals: learning their optimal bidding policy and estimating the expected effect of exposing users to their ads. Typical strategies to accomplish one of these goals tend to ignore the other, creating an apparent tension between the two
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
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presents a new experimental design to perform causal inference on advertising bought through such mechanisms. Our method leverages
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the bid optimization problem faced by advertisers to show that these two objectives can actually be perfectly aligned. By framing the advertiser's problem as
first- and second-price auctions, which are ubiquitous in RTB systems, embedded within
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problem, we propose
setup for online adaptive experimentation. We implement it via
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concurrently learns the
estimates causal effects of advertising while minimizing the costs of experimentation to the advertiser by simultaneously learning the
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and estimates the expected effect of displaying the ad while minimizing economic losses from potential sub-optimal bidding
that maximizes her expected payoffs from auction participation