# CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

arXiv: https://arxiv.org/abs/2201.00689 · HTML: https://mlsanity.com/paper/2201.00689/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [Variational Recurrent Auto-Encoders](https://mlsanity.com/paper/1412.6581/) (2014)
- [Learning Weighted Representations for Generalization Across Designs](https://mlsanity.com/paper/1802.08598/) (2018)
- [Attribution Modeling Increases Efficiency of Bidding in Display Advertising](https://mlsanity.com/paper/1707.06409/) (2017)
- [A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves](https://mlsanity.com/paper/1608.05182/) (2016)
- [Deep Neural Net with Attention for Multi-channel Multi-touch Attribution](https://mlsanity.com/paper/1809.02230/) (2018)
- [CAMTA: Causal Attention Model for Multi-touch Attribution](https://mlsanity.com/paper/2012.11403/) (2020)
- [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://mlsanity.com/paper/1808.03737/) (2018)
- [Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network](https://mlsanity.com/paper/1902.00215/) (2019)
- [Interpretable Deep Learning Model for Online Multi-touch Attribution](https://mlsanity.com/paper/2004.00384/) (2020)

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

- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [CausalMMM: Learning Causal Structure for Marketing Mix Modeling](https://mlsanity.com/paper/2406.16728/) (2024)
- [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)
- [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)
- [Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss](https://mlsanity.com/paper/2606.03878/) (2026)
- [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)
- [Attribution Markets: A Fisher-Market Formulation for Fractional Credit Assignment Between Planned Tasks and Performed Actions](https://mlsanity.com/paper/2607.20694/) (2026)
- [Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?](https://mlsanity.com/paper/2608.28649/) (2026)

## Abstract (arXiv)

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased, i.e., it can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the exposed advertisements are recommended according to user preferences. This confounding bias of users would lead to an out-of-distribution (OOD) problem in the counterfactual prediction and cause concept drift in attribution. In this paper, we define the causal MTA task and propose CausalMTA to eliminate the influence of user preferences. It systemically eliminates the confounding bias from both static and dynamic preferences to learn the conversion prediction model using historical data. We also provide a theoretical analysis to prove CausalMTA can learn an unbiased prediction model with sufficient data. Extensive experiments on both public datasets and the impression data in an e-commerce company show that CausalMTA not only achieves better prediction performance than the state-of-the-art method but also generates meaningful attribution credits across different advertising channels.