这篇论文教电商平台怎么在多个营销渠道间分钱，用ReAlloc框架在淘宝实测同时涨订单和收入，比传统预测-优化方法更稳。
论文提出ReAlloc，一个用于多通道预算分配的快速-慢速因果框架。它通过正交教师从短期日志提取无偏局部梯度，再由解释引导学生蒸馏为长期边际收益场。在淘宝平台的大规模在线A/B测试中，ReAlloc同时提升了支付订单数和收入。与标准预测-优化方法相比，该方法能处理跨渠道替代效应并避免外推。
Multi-channel Uplift Policy Learning
E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize business utility. However, standard predict-then-optimize (PTO) paradigms fail in this compositional space due to observational confounding and severe extrapolation. We formulate this challenge as a simplex-constrained uplift decision problem and propose ReAlloc, a fast-slow causal framework. Specifically, an agile Orthogonal Teacher extracts unbiased local gradients from short-term logs, while an Explanation-Guided Student distills them into a structured marginal field over long-term horizons. This design enables support-aware, conservative decisions that capture cross-channel substitutions. Extensive simulations and large-scale online A/B tests on Taobao platform demonstrate that ReAlloc achieves simultaneous lifts in both pay order and income.