Structure learning is a research area within machine learning and statistical modeling that focuses on the problem of inferring the underlying structure of a system based on observed data. This can involve identifying relationships, dependencies, causal connections, or hierarchies between variables or components in a dataset. Structure learning is commonly used in fields such as biology, social sciences, finance, and engineering to uncover patterns and insights in complex systems. It often involves techniques such as Bayesian networks, graphical models, and causal inference to model and estimate relationships between variables.