Multi-touch attribution (MTA) currently plays a pivotal role in achieving a fair estimation of the contributions of each advertising touchpoint to-wards conversion behavior, deeply influencing budget allocation and advertising recommenda-tion. Previous works attempted to eliminate the bias caused by user preferences to achieve the unbiased assumption of the conversion model. The multi-model collaboration method is not ef-ficient, and the complete elimination of user in-fluence also eliminates the causal effect of user features on conversion, resulting in limited per-formance of the conversion model. This paper re-defines the causal effect of user features on con-versions and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA (DCRMTA). Our model focuses on extracting causa features between conversions and users while eliminating confounding variables. Fur-thermore, extensive experiments demonstrate DCRMTA's superior performance in converting prediction across varying data distributions, while also effectively attributing value across dif-ferent advertising channels.

翻译：多元触达归因（MTA）在当前广告效果评估中扮演着关键角色，旨在公平估算各广告触达点对转化行为的贡献，深刻影响预算分配与广告推荐策略。既有研究试图消除用户偏好导致的偏差以实现转化模型的无偏假设，但多模型协同方法效率低下，且完全消除用户影响的同时去除了用户特征对转化的因果效应，导致转化模型性能受限。本文重新定义用户特征对转化的因果效应，并提出一种新型端到端方法——深度因果表示多元触达归因（DCRMTA）。该模型聚焦于提取转化与用户间的因果特征，同时消除混杂变量。此外，大量实验表明，DCRMTA在不同数据分布下均展现出卓越的转化预测性能，并能有效实现跨广告渠道的价值归因。