Greedy Equivalence Search

Greedy Equivalence Search (GES) is a widely used algorithm for learning the structure of Bayesian networks, aiming to identify causal relationships from observational data. Current research focuses on extending GES to handle nonparametric models and high-dimensional datasets, often employing distributed computing or incorporating prior knowledge to improve efficiency and accuracy. These advancements are significant because they enhance the applicability of GES to complex real-world problems in various fields, including causal inference and data analysis where large datasets with continuous variables are common. The ongoing refinement of GES and its application to diverse data types promises to significantly improve our ability to uncover causal relationships from data.