Causality Analysis

Causality analysis aims to identify cause-and-effect relationships within data, moving beyond simple correlation to understand underlying mechanisms. Current research focuses on applying this to diverse fields, leveraging techniques like Bayesian networks, graph neural networks, and deep learning models to extract causal relationships from complex data, including text, time series, and brain networks. This work is crucial for improving the interpretability and reliability of machine learning models, enabling more robust decision-making in areas such as healthcare, climate science, and cybersecurity, and facilitating the development of more trustworthy AI systems.