Diverse and Sparse Mixture-of-Experts for Causal Subgraph–Based Out-of-Distribution Graph Learning

Abstract

Current state-of-the-art methods for out-of-distribution (OOD) generalization lack the ability to effectively address datasets with heterogeneous causal subgraphs at the instance level. Existing approaches that attempt to handle such heterogeneity either rely on data augmentation, which risks altering label semantics, or impose causal assumptions whose validity in real-world datasets is uncertain. We introduce a novel *Mixture-of-Experts (MoE)* framework that can model heterogeneous causal subgraphs without relying on restrictive assumptions. Our key idea is to address instance-level heterogeneity by enforcing semantic *diversity* among experts, each generating a distinct causal subgraph, while a learned gate assigns *sparse* weights that adaptively focus on the most relevant experts for each input. Our theoretical analysis shows that these two properties jointly reduce OOD error. In practice, our experts are scalable and do not require environment labels. Empirically, our framework achieves strong performance on the GOOD benchmark across both synthetic and real-world structural shifts.