Cost-Effective Incentive Allocation via Structured Counterfactual Inference
This addresses a practical marketing campaign problem for central agents, but it is incremental as it builds on existing policy optimization frameworks with added constraints.
The paper tackled the problem of allocating financial incentives to customers under budget constraints using bandit feedback, and developed a two-step method that achieved significant improvement on synthetic and real datasets.
We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets.