  Conventional bidding strategies for online display ad auction heavily relies
on observed performance indicators such as clicks or conversions. A bidding
strategy naively pursuing these easily observable metrics, however, fails to
optimize the profitability of the advertisers. Rather, the bidding strategy
that leads to the maximum revenue is a strategy pursuing the performance lift
of showing ads to a specific user. Therefore, it is essential to predict the
lift-effect of showing ads to each user on their target variables from observed
log data. However, there is a difficulty in predicting the lift-effect, as the
training data gathered by a past bidding strategy may have a strong bias
towards the winning impressions. In this study, we develop Unbiased Lift-based
Bidding System, which maximizes the advertisers' profit by accurately
predicting the lift-effect from biased log data. Our system is the first to
enable high-performing lift-based bidding strategy by theoretically alleviating
the inherent bias in the log. Real-world, large-scale A/B testing successfully
demonstrates the superiority and practicability of the proposed system.
