Conditional Independence

Conditional independence, the absence of a statistical relationship between variables given a third, is central to causal inference and machine learning, enabling the identification of causal structures and the development of fairer, more robust models. Current research focuses on developing efficient algorithms for testing conditional independence, particularly in high-dimensional settings with mixed data types and latent variables, often employing kernel methods, optimal transport, and constraint-based approaches like the PC algorithm and its variants. These advancements are crucial for improving the accuracy and interpretability of machine learning models, as well as for uncovering causal relationships in complex systems across diverse scientific domains.