The quest for causality in Artificial Intelligence is about understanding the 'why' behind patterns, enabling more informed and actionable insights. Graph Neural Networks (GNNs) and the Deep Graph Library (DGL) are promising avenues in this direction. While primarily designed for relational reasoning, their inherent structure and capabilities can be harnessed to get us closer to Causal AI. Unraveling the intricate web of causality in vast datasets has been a longstanding objective for data scientists and researchers. While correlation reveals relationships, causation uncovers the driving forces behind phenomena. In this context, Graph Neural Networks (GNNs) and the Deep Graph Library (DGL) offer a promising route to explore and harness causality. Let's delve deeper into their potential.
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