Causal Insight

Causal insight research focuses on moving beyond simple correlations to understand true cause-and-effect relationships within complex systems. Current efforts leverage various machine learning models, including graph neural networks, causal inference methods (like Double Machine Learning), and large language models to extract causal knowledge from observational data, often addressing confounding variables through techniques such as backdoor adjustment. This work has significant implications across diverse fields, improving decision-making in areas like education, traffic prediction, and cloud system reliability by enabling more accurate modeling and more effective interventions.