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

TL;DR

This paper introduces a novel causal inference framework for marketing attribution that relies solely on aggregated impression data, enabling privacy-preserving, scalable, and accurate channel influence estimation without user-level data.

Contribution

It presents a new CDA framework combining temporal causal discovery and causal effect estimation that infers channel influence from aggregated data, bypassing privacy restrictions.

Findings

Achieves 9.50% RMSE with true causal graph

Achieves 24.23% RMSE with predicted graph

Captures cross-channel dependencies effectively

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.