Reinforcement Learning for Uplift Modeling
Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong
arXiv:1811.10158v23 citations
Originality Incremental advance
AI Analysis
This addresses the problem of modeling treatment effects for personalized interventions, but appears incremental as it applies an existing RL framework to a known task.
The paper tackled uplift modeling by reformulating it as a Markov Decision Process, achieving significant improvement over previous methods in experiments on synthetic and real-world datasets.
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that our method can achieve significant improvement over previous methods.