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arXiv AI By Jiaming Tang, Jingxuan Wen, Liping Jing

DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution

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The paper introduces DCRMTA, an end‑to‑end framework for deep causal representation learning in multi‑touch attribution (MTA). It addresses a flaw in existing deconfounding pipelines that discard user‑related causal signals by explicitly preserving the causal impact of user features. Using structural causal modeling and adaptive counterfactual attention, DCRMTA produces invariant user representations and achieves up to a 5.2% relative improvement in PR‑AUC on real industrial datasets, while offering robust Shapley‑based credit allocations across marketing channels.

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