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

• URL: http://arxiv.org/abs/2512.21211v1
• Date: Wed, 24 Dec 2025 14:51:12 GMT
• Title: Causal-driven attribution (CDA): Estimating channel influence without user-level data
• Abstract summary: This paper introduces a Causal-Driven Attribution framework that infers channel influence using only aggregated impression-level data.<n>We show that the framework achieves an average relative RMSE of 9.50% when given the true causal graph, and 24.23% when using the predicted graph.
• Score: 0.47055198199603737
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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