Cost-Effective Incentive Allocation via Structured Counterfactual Inference


  • Romain Lopez University of California, Berkeley
  • Chenchen Li Shanghai Jiao Tong University
  • Xiang Yan Shanghai Jiao Tong University
  • Junwu Xiong Ant Financial Services Group
  • Michael Jordan University of California, Berkeley
  • Yuan Qi Ant Financial Services Group
  • Le Song Ant Financial Services Group



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.




How to Cite

Lopez, R., Li, C., Yan, X., Xiong, J., Jordan, M., Qi, Y., & Song, L. (2020). Cost-Effective Incentive Allocation via Structured Counterfactual Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4997-5004.



AAAI Technical Track: Machine Learning