联合优化去偏点击率和优惠券营销提升：一个统一因果框架
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
AI总结：
针对优惠券等营销干预导致的点击率预测偏差，提出统一多值处理网络UniMVT，通过反事实推断同时实现去偏点击率预测和提升估计。
摘要：
在线广告中，优惠券等营销干预会引入显著的混杂偏差，影响点击率（CTR）预测。观察到的点击反映了用户内在偏好与干预带来的提升的混合。这导致传统模型对基础CTR校准不准确，从而扭曲下游排序和计费决策。此外，营销干预通常作为多值处理，具有不同幅度，给CTR预测增加了额外复杂性。为解决这些问题，我们提出了统一多值处理网络（UniMVT）。具体来说，UniMVT从处理敏感表示中解耦混杂因素，使得全空间反事实推断模块能够联合重建去偏的基础CTR和强度-响应曲线。为处理多值处理的复杂性，UniMVT采用辅助强度估计任务来捕获处理倾向，并设计一个单位提升目标来归一化干预效果。这确保了在连续优惠券价值谱上的可比较估计。UniMVT同时实现了用于准确系统校准的去偏CTR预测和用于激励分配的精确提升估计。在合成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。
英文摘要：
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 Unified Multi-Valued Treatment 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
