TY - JOUR AU - Lopez, Romain AU - Li, Chenchen AU - Yan, Xiang AU - Xiong, Junwu AU - Jordan, Michael AU - Qi, Yuan AU - Song, Le PY - 2020/04/03 Y2 - 2024/03/28 TI - Cost-Effective Incentive Allocation via Structured Counterfactual Inference JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.5939 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5939 SP - 4997-5004 AB - <p>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.</p> ER -