  The exploration of Graph Neural Networks (GNNs) for processing
graph-structured data has expanded, particularly their potential for causal
analysis due to their universal approximation capabilities. Anticipated to
significantly enhance common graph-based tasks such as classification and
prediction, the development of a causally enhanced GNN framework is yet to be
thoroughly investigated. Addressing this shortfall, our study delves into nine
benchmark graph classification models, testing their strength and versatility
across seven datasets spanning three varied domains to discern the impact of
causality on the predictive prowess of GNNs. This research offers a detailed
assessment of these models, shedding light on their efficiency, and flexibility
in different data environments, and highlighting areas needing advancement. Our
findings are instrumental in furthering the understanding and practical
application of GNNs in diverse datacentric fields
