发表机构
Peking University; Alibaba Group; Beihang University; CUHK-shenzhen（北京大学; 阿里巴巴集团; 北京航空航天大学; 香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电商多渠道营销预算分配的预测后优化范式失效问题，提出快慢因果框架ReAlloc，经淘宝模拟与A/B测试验证可同时提升支付订单量和收入。
AI 中文摘要
电商平台需在多个渠道间分配固定营销预算以最大化业务效用，但标准的预测后优化（PTO）范式因观测混淆和严重外推在该组合空间失效。我们将此挑战建模为单纯形约束的提升决策问题，提出ReAlloc这一快慢因果框架：敏捷正交教师从短期日志提取无偏局部梯度，解释引导学生将其提炼为长期结构化边际场，该设计支持感知、保守决策并捕捉跨渠道替代关系。在淘宝平台开展的大量模拟和大规模在线A/B测试表明，ReAlloc同时实现了支付订单量和收入的提升。
英文摘要
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.
