# LiDDA: Data Driven Attribution at LinkedIn

arXiv: https://arxiv.org/abs/2505.09861 · HTML: https://mlsanity.com/paper/2505.09861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [On Identifiability in Transformers](https://mlsanity.com/paper/1908.04211/) (2019)
- [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)
- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)

## Built on by (most cited first, 3 of 3)

- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms](https://mlsanity.com/paper/2603.02184/) (2026)
- [Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning](https://mlsanity.com/paper/2602.13134/) (2026)

## Abstract (arXiv)

Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence and vital to any marketing business and advertising platform. In this paper, we introduce a unified transformer-based attribution approach that can handle member-level data, aggregate-level data, and integration of external macro factors. We detail the large scale implementation of the approach at LinkedIn, showcasing significant impact. We also share learnings and insights which are broadly applicable to the marketing and ad tech fields.