ALM-MTA uses front-door causal inference with an adversarially trained mediator and contrastive learning to improve multi-touch attribution, reporting gains in DAU, creator activity, exposure efficiency, AUUC, and upload AUC on a 400M DAU platform.
Causal meta-learning with multi-view graphs for cold-start recommendation.ACM Transactions on Knowledge Discovery from Data, 2025a
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ALM-MTA:Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization
ALM-MTA uses front-door causal inference with an adversarially trained mediator and contrastive learning to improve multi-touch attribution, reporting gains in DAU, creator activity, exposure efficiency, AUUC, and upload AUC on a 400M DAU platform.