Causal Knowledge

Causal knowledge research aims to understand and model cause-and-effect relationships, moving beyond mere correlation to uncover underlying mechanisms. Current efforts focus on developing and applying causal knowledge graphs, often integrated with large language models and graph neural networks, to extract and reason with causal information from diverse data sources like text, time series, and financial statements. This work has significant implications for improving the explainability and robustness of AI systems, enhancing decision-making in various fields, and advancing scientific understanding across disciplines.