Association Test

Association tests aim to identify relationships between variables, a fundamental task across diverse scientific fields. Current research focuses on improving the accuracy and interpretability of these tests, particularly when dealing with high-dimensional data and complex relationships, employing methods like kernel-based neural networks, random forests, and graph neural networks to model non-linear associations and account for confounding factors. These advancements are crucial for enhancing the reliability of findings in various domains, from genetics and medical imaging to energy consumption forecasting and natural language processing, ultimately leading to more robust and insightful scientific discoveries and improved applications.