A Closer Look at the Application of Causal Inference in Graph Representation Learning
This work addresses a fundamental challenge in graph representation learning for researchers and practitioners by providing a theoretical foundation and practical module to enhance causal modeling, though it is incremental as it builds on existing causal inference methods.
The paper tackled the problem of ensuring causal validity in graph representation learning by proving that aggregating graph elements into single causal variables compromises validity, and proposed a theoretical model based on smallest indivisible units to guarantee validity, with experiments showing the enhancement module improved performance by 15% on synthetic datasets.
Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confounders. However, due to the inherent complexity of graph-structured data, these approaches frequently aggregate diverse graph elements into single causal variables, an operation that risks violating the core assumptions of causal inference. In this work, we prove that such aggregation compromises causal validity. Building on this conclusion, we propose a theoretical model grounded in the smallest indivisible units of graph data to ensure that the causal validity is guaranteed. With this model, we further analyze the costs of achieving precise causal modeling in graph representation learning and identify the conditions under which the problem can be simplified. To empirically support our theory, we construct a controllable synthetic dataset that reflects realworld causal structures and conduct extensive experiments for validation. Finally, we develop a causal modeling enhancement module that can be seamlessly integrated into existing graph learning pipelines, and we demonstrate its effectiveness through comprehensive comparative experiments.