In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of users' intrinsic preferences and the uplift induced by these interventions. This causes conventional models to miscalibrate base CTRs, which distorts downstream ranking and billing decisions. Furthermore, marketing interventions often operate as multi-valued treatments with varying magnitudes, introducing additional complexity to CTR prediction. To address these issues, we propose the \textbf{Uni}fied \textbf{M}ulti-\textbf{V}alued \textbf{T}reatment Network (UniMVT). Specifically, UniMVT disentangles confounding factors from treatment-sensitive representations, enabling a full-space counterfactual inference module to jointly reconstruct the debiased base CTR and intensity-response curves. To handle the complexity of multi-valued treatments, UniMVT employs an auxiliary intensity estimation task to capture treatment propensities and devise a unit uplift objective that normalizes the intervention effect. This ensures comparable estimation across the continuous coupon-value spectrum. UniMVT simultaneously achieves debiased CTR prediction for accurate system calibration and precise uplift estimation for incentive allocation. Extensive experiments on synthetic and industrial datasets demonstrate UniMVT's superiority in both predictive accuracy and calibration. Furthermore, real-world A/B tests confirm that UniMVT significantly improves business metrics through more effective coupon distribution.

翻译：在在线广告中，优惠券等营销干预措施会给点击率（CTR）预测带来显著的混淆偏差。观测到的点击行为反映了用户内在偏好与干预所诱导的提升效应的混合。这导致传统模型对基准CTR的校准失准，进而扭曲下游的排序和计费决策。此外，营销干预通常作为具有不同强度的多值处理变量运行，这为CTR预测引入了额外的复杂性。为解决这些问题，我们提出了\textbf{统一多值处理网络}（UniMVT）。具体而言，UniMVT将混淆因素从处理敏感的表示中解耦，使一个全空间反事实推断模块能够联合重构去偏的基准CTR和强度-响应曲线。为处理多值处理的复杂性，UniMVT采用一个辅助的强度估计任务来捕获处理倾向，并设计了一个单元提升目标以归一化干预效果。这确保了在连续的优惠券价值谱系上进行可比较的估计。UniMVT同时实现了去偏的CTR预测以进行准确的系统校准，以及精确的提升效应估计以用于激励分配。在合成数据集和工业数据集上的大量实验证明了UniMVT在预测准确性和校准方面的优越性。此外，真实世界的A/B测试证实，UniMVT通过更有效的优惠券分发显著改善了业务指标。