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
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