Causal Reasoning Graph Neural Networks in Deep Learning

Abstract

This paper proposes a novel approach to deep learning that integrates causal reasoning with Graph Neural Networks (GNNs). The core idea is to construct a neural network model capable of learning causal relationships and performing inference based on those relationships. Traditional deep learning models often struggle to understand and represent causal relationships, leading to limitations in interpretability and robustness. Our approach utilizes causal graphs to analyze input data, identifying potential causal links. Subsequently, a Graph Neural Network is employed to learn the relationships between nodes within the graph, explicitly incorporating these causal relationships into the learning process. This allows the model to perform tasks such as predicting the impact of a causal factor and determining the direct cause of an event. The integration of causal reasoning into GNNs enhances the model's ability to understand and reason about complex systems, ultimately improving both its interpretability and its resilience to spurious correlations. The key contribution lies in providing a framework for building interpretable and robust deep learning models by explicitly modeling causal dependencies.