Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization algorithm is popular in practice. To enhance performance in the online environment, the framework needs to be improved to address the complexities caused by temporal dataset shift. This paper focuses on capturing the dataset shift from user behavior and domain distribution changing over time. We propose an Incremental Causal Effect with Proxy Knowledge Distillation (ICE-PKD) framework to tackle this challenge. The ICE-PKD framework includes two components: (i) a multi-treatment uplift network that eliminates confounding bias using counterfactual regression; (ii) an incremental training strategy that adapts to the temporal dataset shift by updating with the latest data and protects generalization via replay-based knowledge distillation. We also revisit the uplift modeling metrics and introduce a novel metric for more precise online evaluation in multiple treatment scenarios. Extensive experiments on both simulated and online datasets show that the proposed framework achieves better performance. The ICE-PKD framework has been deployed in the marketing system of Huaxiaozhu, a ride-hailing platform in China.

本研究解决了在线环境中因时间数据集转移所带来的复杂性，旨在提升因果效应估计在市场优化中的表现。我们提出的增量因果效应与代理知识蒸馏（ICE-PKD）框架通过多处理提升网络和增量训练策略，能够有效应对用户行为和领域分布的变化，实验结果表明该框架在实际应用中表现优越，已在中国的网约车平台华小猪中部署。

数据集转移流的持续因果效应估计