DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification

TL;DR

DeCaf introduces a causal decoupling framework based on Structural Causal Models to improve GNNs' robustness against distribution shifts in node classification tasks, addressing limitations of prior invariant learning methods.

Contribution

The paper presents a novel causal decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings to enhance GNN generalization under distribution shifts.

Findings

DeCaf outperforms existing methods on real-world datasets.

It effectively mitigates various types of distribution shifts.

Theoretical analysis confirms its robustness in different scenarios.

Abstract

Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the generalizability of GNNs on out-of-distribution (OOD) test data. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process, which do not adequately reflect the actual dynamics of distribution shifts in graphs. In this paper, we introduce a more realistic graph data generation model using Structural Causal Models (SCMs), allowing us to redefine distribution shifts by pinpointing their origins within the generation process. Building on this, we propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings. We provide a detailed theoretical framework that shows how our approach can effectively mitigate the impact of various distribution shifts. We evaluate DeCaf across both real-world and synthetic datasets that demonstrate different patterns of shifts, confirming its efficacy in enhancing the generalizability of GNNs.