Causal Bayes Nets

Causal Bayesian Networks are visual tools that model the relationships between variables, showing how one factor can influence another. They use nodes to represent variables and arrows to indicate cause-and-effect connections. By analyzing these networks, statisticians and researchers can understand how changes in one variable might lead to changes in others, helping to identify causes, predict outcomes, and make informed decisions. They combine probability with causal reasoning to better capture real-world processes and support evidence-based conclusions.