Causal-Driven Attribution (CDA): Estimating Channel Influence Without User-Level Data
Published:Dec 25, 2025 05:00
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Analysis
This paper introduces a novel approach to marketing attribution called Causal-Driven Attribution (CDA). CDA addresses the growing challenge of data privacy by estimating channel influence using only aggregated impression-level data, eliminating the need for user-level tracking. The framework combines temporal causal discovery with causal effect estimation, offering a privacy-preserving and interpretable alternative to traditional path-based models. The results on synthetic data are promising, showing good accuracy even with imperfect causal graph prediction. This research is significant because it provides a potential solution for marketers to understand channel effectiveness in a privacy-conscious world. Further validation with real-world data is needed.
Key Takeaways
Reference
“CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.”