693JJ31950023Traditional safety modeling efforts primarily focus on accurately estimating crash frequencies or rates. The true relationships between crashes and potential causal factors are not always easily discernible from safety models. While a model consisting of multiple causal factors may produce accurate estimates of crash measures, it may not accurately explain all causal relationships. Knowing the true cause-and-effect relationships is important while choosing countermeasures to address safety problems. This Exploratory Advanced Research Program project developed a framework to generate realistic artificial data (RAD) datasets that mimic the known causal relationships between contributing factors and crashes. The proposed framework...