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arXiv · 2607.28182

Multi-channel Uplift Policy Learning

Changjian Liu·Tianyu Wang·Xiaoxuan Deng·WenTao Zhu·Yuwei Xu·Jungqi Jin·Yong Gao·Chuan Yu·Jian Xu·Bo Zheng
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Abstract

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.

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Changjian Liu, Tianyu Wang, Xiaoxuan Deng, WenTao Zhu, Yuwei Xu, Jungqi Jin, Yong Gao, Chuan Yu, Jian Xu, Bo Zheng. 2026-07-30. Multi-channel Uplift Policy Learning. https://arxiv.org/abs/2607.28182

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