Causal Inference Explanations for Graph Neural Networks

The increasing popularity of Artificial Intelligence, notably Deep Learning, has prompted its application across diverse real-world scenarios. Graph Neural Networks, a type of Deep Learning model developed for graph-structured data, have gained significant popu- larity owing to their versatility and efficacy. Despite their high accuracy and performance, contemporary Deep Learning models often function as black box systems, lacking trans- parency and trustworthiness crucial for deployment in critical decision-making domains. In response, Explainable Artificial Intelligence has emerged, aiming to enhance the trustworthiness of black box models by devising explanation methods that clarify their inner workings. However, prevalent explanation techniques predominantly leverage correlation and association rather than employing causality, a significant aspect of human comprehension. In this thesis, we propose a novel explanation method using causal inference developed specifically for Graph Neural Networks. Our approach seeks to illuminate the decision- making process of Graph Neural Networks, thereby augmenting their transparency and trustworthiness. To show the generality of our approach we do a benchmark evaluation of our method against state-of-the-art explanation methods on a variety of datasets, tasks and Graph Neural Networks architectures. Furthermore, we apply and evaluate our explanation method in two real-world use cases: medical referral recommendation and space object conjunction prediction. In both cases, we demonstrate the applicability of our method and discuss its limitations.