mlsanityData to 14 Oct 2026

LiDDA: Data Driven Attribution at LinkedIn

arXiv 2505.09861 ↗First posted 14 May 2025cs.LG cs.AI cs.IRcited by 3

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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.

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

8 of 29 references.

References · 8

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Attention is not Explanationcited by 929
  4. 2019Attention is not not Explanationcited by 636
  5. 2019On Identifiability in Transformerscited by 116
  6. 2018Deep Neural Net with Attention for Multi-channel Multi-touch Attributioncited by 12
  7. 2020CAMTA: Causal Attention Model for Multi-touch Attributioncited by 12
  8. 2021CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attributioncited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.