Fulfillment of NASA's System-Wide Safety and Assurance Technology (SSAT) project at NASA requires leveraging vast amounts of data into actionable knowledge. Models of accident causation describe a causation chain. The chain would be better understood by examining the large amounts of "everyday" flight data, not just data proximal to high-profile incidents. This proposal is focused on the detection and prediction of more common flight errors or conditions which are necessary for aviation incidents. However, data sets containing safety information are (1) large, (2) distributed, and (3) heterogeneous, making analysis difficult. In order to address these challenges, we propose Causal Models for Safety and Assurance Technologies (CM-SAT). CM-SAT will mine large, distributed, heterogeneous data systems for causal relationships about flight safety. The system will identify causal schema within the data that characterize conditions related to the aircraft and environment that are predictive of failures. CM-SAT will detect causal relationships at varying levels of granularity (e.g. relationships which are unique to a particular flight, to a particular aircraft model, or to a particular fleet). It will leverage state-of-the-art distributed meta-reasoning, which will direct the causal schema learning algorithms to detect and validate causal relationships in different parts of the distributed data sets.