mlsanityData to 7 Oct 2026

Causality-based CTR Prediction using Graph Neural Networks

arXiv 2301.12762 ↗First posted 30 Jan 2023cs.IR cs.AI cs.LGcited by 9

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Abstract (arXiv)

As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete graph, while ignoring causal relationships among features, which results in a huge drop in the performance on out-of-distribution data. This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising. In our model, a structured representation learning method (GraphFwFM) is designed to capture high-order representations on feature graph based on causal discovery among field features in gated graph neural networks (GGNNs), and GraphSAGE is employed to obtain graph representations of users and ads. Experiments conducted on three public datasets demonstrate the superiority of Causal-GNN in AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order representations on causal feature graph.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 47 of its 82 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

47 of 82 references.

References · 47

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35 more
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Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 3.

9 papers.

  1. 2024Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directionscited by 13
  2. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  3. 2023Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agendacited by 1
  4. 2023Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledgecited by 1
  5. 2023Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graphcited by 1
  6. 2024Graph Neural Machine: A New Model for Learning with Tabular Datacited by 0
  7. 2025Cooperative Causal GraphSAGEcited by 0
  8. 2025Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directionscited by 0
  9. 2025Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectivescited by 0