  Graph NAS has emerged as a promising approach for autonomously designing GNN
architectures by leveraging the correlations between graphs and architectures.
Existing methods fail to generalize under distribution shifts that are
ubiquitous in real-world graph scenarios, mainly because the graph-architecture
correlations they exploit might be spurious and varying across distributions.
We propose to handle the distribution shifts in the graph architecture search
process by discovering and exploiting the causal relationship between graphs
and architectures to search for the optimal architectures that can generalize
under distribution shifts. The problem remains unexplored with following
challenges: how to discover the causal graph-architecture relationship that has
stable predictive abilities across distributions, and how to handle
distribution shifts with the discovered causal graph-architecture relationship
to search the generalized graph architectures. To address these challenges, we
propose Causal-aware Graph Neural Architecture Search (CARNAS), which is able
to capture the causal graph-architecture relationship during the architecture
search process and discover the generalized graph architecture under
distribution shifts. Specifically, we propose Disentangled Causal Subgraph
Identification to capture the causal subgraphs that have stable prediction
abilities across distributions. Then, we propose Graph Embedding Intervention
to intervene on causal subgraphs within the latent space, ensuring that these
subgraphs encapsulate essential features for prediction while excluding
non-causal elements. Additionally, we propose Invariant Architecture
Customization to reinforce the causal invariant nature of the causal subgraphs,
which are utilized to tailor generalized graph architectures. Extensive
experiments demonstrate that CARNAS achieves advanced out-of-distribution
generalization ability.
