ALM-MTA:Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization

TL;DR

ALM-MTA introduces a novel causal framework utilizing front-door identification and adversarial learning to improve attribution accuracy in complex recommendation systems, enhancing creator ecosystem metrics.

Contribution

The paper proposes ALM-MTA, a new causal attribution method combining front-door identification with adversarially learned mediators and contrastive learning for large-scale recommendation systems.

Findings

Increases DAU by 0.04% and creators by 0.6% in real-world tests.

Achieves 670% higher unit exposure efficiency.

Outperforms state-of-the-art in grouped AUUC and upload AUC.

Abstract

Consumption Drives Production (CDP) on social platforms aims to deliver interpretable incentive signals for creator ecosystem building and resource utilization improvement, which strongly relies on attribution. In large-scale and complex recommendation systems, the absence of accurate labels together with unobserved confounding renders backdoor adjustments alone insufficient for reliable attribution. To address these problems, we propose Adversarial Learning Mediator based Multi-Touch Attribution (ALM-MTA), an extensible causal framework that leverages front-door identification with an adversarially learned mediator: a proxy trained to distill outcome information to strengthen the causal pathway from treatment to outcome and eliminate shortcut leakage. We then introduce contrastive learning that conditions front-door marginalization on high-match consumption-upload pairs to ensure positivity in large treatment spaces. To assess causality from non-RCT logs, we also incorporate a non-personalized bucketed protocol, estimating grouped uplift and computing AUUC over treatment clusters. Finally, we evaluate ALM-MTA using a real-world recommendation system with 400 million DAU and 30 billion samples. ALM-MTA increases DAU by 0.04% and daily active creators by 0.6%, with unit exposure efficiency increased by 670%. On causal utility, ALM-MTA achieves higher grouped AUUC than the SOTA in every propensity bucket, with a maximum gain of 0.070. In terms of accuracy, ALM-MTA improves upload AUC by 40% compared to SOTA. These results demonstrate that front-door deconfounding with adversarial mediator learning provides accurate, personalized, and operationally efficient attribution for creator ecosystem optimization.