This is what we need – to be clear on our conceptual framework, and to understand the subtle but important distinction between, for example, prediction and causal inference. And not to unthinkingly ...

This paper presents an innovative and integrated approach for predicting and understanding Adverse Drug Reactions (ADRs) by combining Graph Neural Networks (GNNs) with causal inference techniques.

Modeling spatial dependency is crucial to solving traffic prediction tasks; thus, spatial-temporal graph-based models have been widely used in this area in recent years. Existing approaches either ...