Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D)

The Navy seeks technology based on statistical or computational methods to assist in the continued tracking of training performance and proficiency trends as underlying tactical data changes. OASYS, INC. and the ITCS at UAH proposes to exploit the benefits of modeling the underlying cause-effect structure of Navy data, rather than the data itself. This approach makes the model and analytical methods invariant to changes to in the input distribution, allows for the accurate adjustment of counfounding factors, and enables for the prediction of the outcome of data-driven decisions. The benefits of identifying the cause-effect relationships between variables have been known for some time and are used in many scientific fields where accurate decisions are critical. The method of cause-effect discovery in those fields are often through randomized experimental trials, but the development of causal discovery methods that infer causal relationships from uncontrolled data (non-experimental) is an important and growing area of research that shows great promise in data analytics.