Multi-channel Uplift Policy Learning

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.

Machine Learning (cs.LG)

arXiv:2607.28182 [cs.LG]

(or arXiv:2607.28182v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.28182 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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