  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. Previous works attempted to eliminate the bias caused by user
preferences to achieve the unbiased assumption of the conversion model. The
multi-model collaboration method is not ef-ficient, and the complete
elimination of user in-fluence also eliminates the causal effect of user
features on conversion, resulting in limited per-formance of the conversion
model. This paper re-defines the causal effect of user features on con-versions
and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA
(DCRMTA). Our model focuses on extracting causa features between conversions
and users while eliminating confounding variables. Fur-thermore, extensive
experiments demonstrate DCRMTA's superior performance in converting prediction
across varying data distributions, while also effectively attributing value
across dif-ferent advertising channels.
