Bayesian Additive Regression Tree

Bayesian Additive Regression Trees (BART) are flexible, non-parametric regression models that combine the strengths of Bayesian inference with tree-based ensembles to achieve accurate predictions and reliable uncertainty quantification. Current research focuses on improving BART's computational efficiency, particularly for large datasets, and extending its capabilities to address causal inference problems (e.g., estimating treatment effects) and diverse data structures (e.g., incorporating co-data or handling multivariate outcomes). These advancements enhance BART's applicability across various fields, including biostatistics, social sciences, and machine learning, by providing robust and interpretable models for complex relationships.