# CAMTA: Causal Attention Model for Multi-touch Attribution

arXiv: https://arxiv.org/abs/2012.11403 · HTML: https://mlsanity.com/paper/2012.11403/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [LiDDA: Data Driven Attribution at LinkedIn](https://mlsanity.com/paper/2505.09861/) (2025)
- [A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution](https://mlsanity.com/paper/2302.06075/) (2023)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution](https://mlsanity.com/paper/2401.08875/) (2024)
- [MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms](https://mlsanity.com/paper/2603.02184/) (2026)
- [Multi-channel Uplift Policy Learning](https://mlsanity.com/paper/2607.28182/) (2026)
- [DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks](https://mlsanity.com/paper/2111.01482/) (2021)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)
- [Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?](https://mlsanity.com/paper/2608.28649/) (2026)

## Abstract (arXiv)

Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict pre-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modelling on the predicted channel attribution.