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arXiv · 2512.21211

Causal-driven attribution (CDA): Estimating channel influence without user-level data

Georgios Filippou·Boi Mai Quach·Diana Lenghel·Arthur White·Ashish Kumar Jha
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Abstract

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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Georgios Filippou, Boi Mai Quach, Diana Lenghel, Arthur White, Ashish Kumar Jha. 2025-12-24. Causal-driven attribution (CDA): Estimating channel influence without user-level data. https://arxiv.org/abs/2512.21211

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