Multi-touch attribution (MTA) currently plays a pivotal role in achieving a fair estimation of the contributions of each advertising touchpoint to-wards conversion behavior, deeply influencing budget allocation and advertising recommenda-tion. Traditional multi-touch attribution methods initially build a conversion prediction model, an-ticipating learning the inherent relationship be-tween touchpoint sequences and user purchasing behavior through historical data. Based on this, counterfactual touchpoint sequences are con-structed from the original sequence subset, and conversions are estimated using the prediction model, thus calculating advertising contributions. A covert assumption of these methods is the un-biased nature of conversion prediction models. However, due to confounding variables factors arising from user preferences and internet recom-mendation mechanisms such as homogenization of ad recommendations resulting from past shop-ping records, bias can easily occur in conversion prediction models trained on observational data. This paper redefines the causal effect of user fea-tures on conversions and proposes a novel end-to-end approach, Deep Causal Representation for MTA (DCRMTA). Our model while eliminating confounding variables, extracts features with causal relations to conversions from users. Fur-thermore, Extensive experiments on both synthet-ic and real-world Criteo data demonstrate DCRMTA's superior performance in converting prediction across varying data distributions, while also effectively attributing value across dif-ferent advertising channels

翻译：多点触控归因（MTA）当前在公平估计各广告触达点对转化行为的贡献中扮演关键角色，深刻影响预算分配与广告推荐。传统多点触控归因方法首先构建转化预测模型，旨在通过学习历史数据中触达点序列与用户购买行为之间的内在关系。在此基础上，从原始序列子集中构建反事实触达点序列，并利用预测模型估算转化率，从而计算广告贡献。这些方法隐含的一个假设是转化预测模型具有无偏性。然而，由于用户偏好产生的混杂变量因素，以及互联网推荐机制（如基于历史购物记录导致的广告推荐同质化）等影响，基于观测数据训练的转化预测模型极易产生偏差。本文重新定义了用户特征对转化的因果效应，并提出了一种新颖的端到端方法——深度因果表示多点触控归因（DCRMTA）。该模型在消除混杂变量的同时，从用户中提取与转化具有因果关系的特征。此外，在合成数据集和真实世界的Criteo数据集上进行的广泛实验表明，DCRMTA在不同数据分布下的转化预测中均展现出优越性能，同时能有效分配不同广告渠道的价值。