A Digital Public Health Framework for Understanding Causal Cognitive Drivers of Adolescent Violence

Adolescent violence is a complex public health issue with significant societal and individual consequences. Traditional research methods often fail to capture the dynamic, multifaceted nature of the cognitive and behavioral drivers of such violence. This study introduces EEG-CauseNet, an innovative Cross-Attention Transformer framework, designed to address this gap by identifying and analyzing the causal cognitive mechanisms underlying adolescent violence. The primary objective of EEG-CauseNet is to integrate and analyze diverse, high-dimensional datasets—such as socio-economic, mental health, and behavioral data—while leveraging advanced causal inference techniques to uncover complex interdependencies between factors influencing violence. Through empirical validation on real-world adolescent datasets, we demonstrate that EEG-CauseNet outperforms traditional models in both predictive accuracy and interpretability. The novel integration of cross-attention mechanisms allows the model to capture dynamic temporal-spatial relationships, offering deeper insights into the underlying drivers of adolescent violence. The outcomes of this research provide actionable insights for public health stakeholders, enabling the development of targeted, technology-driven interventions. This framework has the potential to transform digital public health strategies, offering scalable and impactful solutions to reduce adolescent violence globally.