A Closer Look at the Application of Causal Inference in Graph Representation Learning

TL;DR

This paper investigates the challenges of applying causal inference in graph representation learning, demonstrating that aggregation of graph elements can violate causal assumptions, and proposing a new model based on indivisible graph units to ensure causal validity.

Contribution

It introduces a theoretical model based on minimal graph units, analyzes the costs of causal modeling, and develops an enhancement module for existing graph learning methods.

Findings

Aggregation of graph elements can violate causal assumptions.

A new model based on indivisible units guarantees causal validity.

The proposed module improves causal modeling in graph learning pipelines.

Abstract

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.