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

arXiv 2512.21211 ↗First posted 24 Dec 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

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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References · 18

  1. 2021Granger Causality: A Review and Recent Advancescited by 63
  2. 2019A Unified Framework for Marketing Budget Allocationcited by 33
  3. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  4. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  5. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  6. 2018Deep Neural Net with Attention for Multi-channel Multi-touch Attributioncited by 12
  7. 2020CAMTA: Causal Attention Model for Multi-touch Attributioncited by 12
  8. 2021CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attributioncited by 12
  9. 2020A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applicationscited by 11
  10. 2016Observational-Interventional Priors for Dose-Response Learningcited by 10
  11. 2018Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertisingcited by 9
  12. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
6 more
  1. 2024Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Datacited by 8
  2. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 8
  3. 2020Interpretable Deep Learning Model for Online Multi-touch Attributioncited by 4
  4. 2017Revenue-based Attribution Modeling for Online Advertisingcited by 3
  5. 2023A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attributioncited by 2
  6. 2024From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?cited by 1

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