Local Independence

Local independence, the statistical absence of correlation between variables, is a crucial concept across diverse scientific fields, with current research focusing on its detection and implications in complex systems. Investigations utilize various methods, including statistical independence tests, neural network architectures like transformers and logistic regression, and ensemble techniques to assess independence in settings ranging from sentiment analysis and causal inference to high-dimensional data and reinforcement learning. Understanding and leveraging local independence is vital for improving model accuracy, mitigating biases, and gaining insights into causal relationships within data, impacting fields from machine learning and neuroscience to causal discovery and social sciences.