Attribution modelling lies at the heart of marketing effectiveness, yet most existing approaches depend on user-level path data, which are increasingly inaccessible due to privacy regulations and platform restrictions. This paper introduces a Causal-Driven Attribution (CDA) framework that infers channel influence using only aggregated impression-level data, avoiding any reliance on user identifiers or click-path tracking. CDA integrates temporal causal discovery (using PCMCI) with causal effect estimation via a Structural Causal Model to recover directional channel relationships and quantify their contributions to conversions. Using large-scale synthetic data designed to replicate real marketing dynamics, we show that CDA achieves an average relative RMSE of 9.50% when given the true causal graph, and 24.23% when using the predicted graph, demonstrating strong accuracy under correct structure and meaningful signal recovery even under structural uncertainty. CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.

翻译：归因建模是营销效果评估的核心，然而现有方法大多依赖于用户级路径数据，这些数据因隐私法规和平台限制而日益难以获取。本文提出一种因果驱动归因（CDA）框架，该框架仅使用聚合展示级数据即可推断渠道影响力，无需依赖用户标识符或点击路径追踪。CDA将时序因果发现（采用PCMCI方法）与基于结构因果模型的因果效应估计相结合，以还原定向渠道关系并量化其对转化的贡献。通过使用模拟真实营销动态的大规模合成数据进行验证，本研究表明：在给定真实因果图的情况下，CDA的平均相对均方根误差为9.50%；在使用预测因果图时，误差为24.23%。这证明该方法在正确结构下具有强准确性，即使在结构不确定性下仍能实现有意义的信号还原。CDA能够捕捉跨渠道相互依赖性，同时提供可解释、保护隐私的归因洞察，为传统基于路径的模型提供了可扩展且面向未来的替代方案。