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

arXiv: https://arxiv.org/abs/2512.21211 · HTML: https://mlsanity.com/paper/2512.21211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 111 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [A Unified Framework for Marketing Budget Allocation](https://mlsanity.com/paper/1902.01128/) (2019)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing](https://mlsanity.com/paper/2211.15728/) (2022)
- [The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications](https://mlsanity.com/paper/2412.01953/) (2024)
- [Deep Neural Net with Attention for Multi-channel Multi-touch Attribution](https://mlsanity.com/paper/1809.02230/) (2018)
- [CAMTA: Causal Attention Model for Multi-touch Attribution](https://mlsanity.com/paper/2012.11403/) (2020)
- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Observational-Interventional Priors for Dose-Response Learning](https://mlsanity.com/paper/1605.01573/) (2016)
- [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://mlsanity.com/paper/1808.03737/) (2018)
- [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023)
- [Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data](https://mlsanity.com/paper/2407.13054/) (2024)
- [Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery](https://mlsanity.com/paper/2503.17037/) (2025)
- [Interpretable Deep Learning Model for Online Multi-touch Attribution](https://mlsanity.com/paper/2004.00384/) (2020)
- [Revenue-based Attribution Modeling for Online Advertising](https://mlsanity.com/paper/1710.06561/) (2017)
- [A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution](https://mlsanity.com/paper/2302.06075/) (2023)
- [From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?](https://mlsanity.com/paper/2409.02130/) (2024)

## 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.