A Causal Transformer Multi-Touch Attribution with Dual Debiasing and Explainable Visualization for Advertising Recommendation

Abstract

Multi-touch attribution (MTA) aims to estimate the causal effect of advertising touchpoints on user conversions, providing essential support for budget allocation and advertising recommendation. Existing causal MTA methods often struggle to capture complex temporal dependencies in long touchpoint sequences and fail to adequately address confounding biases arising from both static user profiles and dynamic behaviors. To address these challenges, we propose CT-MTA, an end-to-end causal Transformer framework for multi-touch attribution. CT-MTA integrates a causally masked Transformer to model sequential dependencies, together with a Variational Autoencoder (VAE) and a Gradient Reversal Layer (GRL) to mitigate dual confounding biases. Furthermore, we design a counterfactual attribution module with a Mixture-of-Experts (MoE) architecture to generate personalized and interpretable attribution estimates. Experiments on both synthetic and real-world datasets show that CT-MTA improves conversion prediction Area Under the ROC Curve (AUC) by 1.2% over the state-of-the-art method. In downstream budget allocation tasks, CT-MTA consistently reduces Cost Per Acquisition (CPA) and improves Conversion Rate (CVR) under limited budgets. Qualitative analysis further shows that CT-MTA alleviates over-attribution to historically favored touchpoints, leading to more efficient and fair budget allocation.