DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
This addresses the problem of fair budget allocation and advertising recommendation for marketers, but it is incremental as it builds on existing causal methods.
The paper tackles bias in multi-touch attribution by redefining the causal effect of user features on conversions, proposing DCRMTA, which achieves superior performance in conversion prediction and effective attribution across advertising channels.
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.