While observational data can’t formally prove causality, comprehensive datasets can bring you remarkably close. The more complete the data, the easier it becomes to separate true cause-and-effect relationships from simple correlations.

Our ObjectAnalytics Database™ is designed for fully granular, end-to-end data use—no prior feature selection required. Whether you’re working with millions of patients and billions of clinical or genomic events, or with large-scale production data from sensors, processes, and machinery, our Causal Discovery algorithms leverage the entire dataset to uncover direct and indirect drivers of any target outcome.