Causal Pattern

Causal pattern research aims to identify and quantify cause-and-effect relationships within complex systems, moving beyond simple correlations to understand underlying mechanisms. Current research focuses on developing and applying methods like structural causal models, Granger causality, and various machine learning techniques (e.g., sparse regression, GNNs) to uncover causal patterns in diverse data types, including time series, tabular data, and even images. This work has significant implications for various fields, improving the reliability and interpretability of models in areas such as healthcare, agriculture, finance, and autonomous systems by enabling more robust predictions and informed decision-making.