  Many payment platforms hold large-scale marketing campaigns, which allocate
incentives to encourage users to pay through their applications. To maximize
the return on investment, incentive allocations are commonly solved in a
two-stage procedure. After training a response estimation model to estimate the
users' mobile payment probabilities (MPP), a linear programming process is
applied to obtain the optimal incentive allocation. However, the large amount
of biased data in the training set, generated by the previous biased allocation
policy, causes a biased estimation. This bias deteriorates the performance of
the response model and misleads the linear programming process, dramatically
degrading the performance of the resulting allocation policy. To overcome this
obstacle, we propose a bias correction adversarial network. Our method
leverages the small set of unbiased data obtained under a full-randomized
allocation policy to train an unbiased model and then uses it to reduce the
bias with adversarial learning. Offline and online experimental results
demonstrate that our method outperforms state-of-the-art approaches and
significantly improves the performance of the resulting allocation policy in a
real-world marketing campaign.
