无需用户数据，用汇总数据推算渠道影响力
Causal-driven attribution (CDA): Estimating channel influence without user-level data
- 基于聚合曝光数据，结合因果发现与结构因果模型推断渠道关系
- 在已知真实因果图时相对均方根误差仅9.50%，预测图下为24.23%
- 适合隐私受限场景，提供可解释且可扩展的归因方案
归因建模是衡量营销效果的核心，但现有方法多依赖用户级路径数据，受隐私法规和平台限制日益难以获取。本文提出因果驱动归因（CDA）框架，仅使用聚合曝光数据即可推断渠道影响，无需用户标识或点击路径追踪。CDA融合时间因果发现（PCMCI）与结构因果模型进行因果效应估计，恢复渠道间的方向性关系并量化其对转化的贡献。通过大规模模拟数据验证，当给定真实因果图时，平均相对均方根误差为9.50%；使用预测因果图时为24.23%，表明在结构正确时精度高，在结构不确定时仍能有效捕捉信号。CDA能识别跨渠道相互依赖，提供可解释、隐私保护的归因洞察，是一种可扩展且面向未来的替代方案。
原文摘要 · Abstract (English)
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.
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