Cost-Effective Incentive Allocation via Structured Counterfactual Inference

Abstract

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.

References in corpus (7)

• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Doubly Robust Policy Evaluation and Learning
• Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
• Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
• Learning from Logged Implicit Exploration Data
• Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
• An Adaptive Test of Independence with Analytic Kernel Embeddings