A Causal Transformer Multi-Touch Attribution with Dual Debiasing and Explainable Visualization for Advertising Recommendation
CT-MTA integrates a causally masked Transformer to model sequential dependencies, together with a Variational Autoencoder and a Gradient Reversal Layer to mitigate dual confounding biases, and design a counterfactual attribution module with a Mixture-of-Experts architecture to generate personalized and interpretable at...