Graph Neural Networks (GNNs) are currently used in many real-world applications. With this notable spread, the development of sophisticated techniques for explaining their decisions becomes highly necessary. Although many works have been proposed in the aim of explaining their predictions, on different aspects, such as nodes, edges, and features, they all tend to generate the explanations as subgraphs. In this paper, we will show that relying only on explanatory subgraphs is not sufficient explanation tool, especially that these subgraphs could range from small graphs to untraceable ones within the same model. In this regard, we propose a causal explanation framework based on the rigorous structural model of causality. We show that our framework does not compete with existing explanation framework for GNNs, but rather acts as a complementary approach.