Gaussian DAG

Gaussian DAGs represent causal relationships between variables using directed acyclic graphs, where variables follow Gaussian distributions. Current research focuses on developing efficient Bayesian methods, such as Markov Chain Monte Carlo (MCMC) algorithms, for learning the structure and parameters of these models, including extensions to handle multiple groups and binary responses. This work aims to improve the accuracy and scalability of causal inference from observational data, with applications ranging from analyzing complex biological systems to understanding social and economic phenomena. The optimal sample complexity for learning these models is also a key area of investigation, revealing interesting connections to the learning of undirected graphical models.