Crashes occur because of complex interactions between multiple variables, including driver behavior, environmental context, roadway design, and vehicle dynamics. Data-driven safety analysis (DDSA) models help State and local agencies quantify safety data, identify high-risk roadway features, and predict the effects of proposed safety measures. However, even when a model performs well overall, it may not accurately represent the interactions between variables for a specific location or crash because the underlying relationships in the real world are unknown. One proposed solution is to generate realistic artificial datasets (RAD) with predetermined safety relationships built into them. Since these are known, the RAD can serve as a testbed, revealing how well a model reflects those underlying cause and effect relationships.