  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.
