# Interpretable Deep Learning Model for Online Multi-touch Attribution

arXiv: https://arxiv.org/abs/2004.00384 · HTML: https://mlsanity.com/paper/2004.00384/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Mamba-FETrack: Frame-Event Tracking via State Space Model](https://mlsanity.com/paper/2404.18174/) (2024)
- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution](https://mlsanity.com/paper/2401.08875/) (2024)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)

## Abstract (arXiv)

In online advertising, users may be exposed to a range of different advertising campaigns, such as natural search or referral or organic search, before leading to a final transaction. Estimating the contribution of advertising campaigns on the user's journey is very meaningful and crucial. A marketer could observe each customer's interaction with different marketing channels and modify their investment strategies accordingly. Existing methods including both traditional last-clicking methods and recent data-driven approaches for the multi-touch attribution (MTA) problem lack enough interpretation on why the methods work. In this paper, we propose a novel model called DeepMTA, which combines deep learning model and additive feature explanation model for interpretable online multi-touch attribution. DeepMTA mainly contains two parts, the phased-LSTMs based conversion prediction model to catch different time intervals, and the additive feature attribution model combined with shaley values. Additive feature attribution is explanatory that contains a linear function of binary variables. As the first interpretable deep learning model for MTA, DeepMTA considers three important features in the customer journey: event sequence order, event frequency and time-decay effect of the event. Evaluation on a real dataset shows the proposed conversion prediction model achieves 91% accuracy.