The optimal bid in a second-price ad auction equals the conditional average treatment effect of the ad, and the paper's Thompson-sampling algorithm learns that bid and therefore the ad effect while reducing experimentation cost.
A/B Testing of Auctions
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abstract
For many application areas A/B testing, which partitions users of a system into an A (control) and B (treatment) group to experiment between several application designs, enables Internet companies to optimize their services to the behavioral patterns of their users. Unfortunately, the A/B testing framework cannot be applied in a straightforward manner to applications like auctions where the users (a.k.a., bidders) submit bids before the partitioning into the A and B groups is made. This paper combines auction theoretic modeling with the A/B testing framework to develop methodology for A/B testing auctions. The accuracy of our method %, assuming the auction is directly comparable to ideal A/B testing where there is no interference between A and B. Our results are based on an extension and improved analysis of the inference method of Chawla et al. (2014).
fields
cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Online Causal Inference for Advertising in Real-Time Bidding Auctions
The optimal bid in a second-price ad auction equals the conditional average treatment effect of the ad, and the paper's Thompson-sampling algorithm learns that bid and therefore the ad effect while reducing experimentation cost.