Background

As the volume of large-scale data generated in both research and commercial settings rapidly expands, accessing key insight on causal connections in a reliable and quick manner is increasingly crucial. Computational tools that accomplish this task will be useful in applications ranging from accurately modeling complex diseases such as cancer and Alzheimer’s disease to predicting how stock prices influence each other. However, currently available methods of analysis are restrictive, as they require individual components to be of similar type and fail to resolve causal relationships accurately while establishing association. In addition, they require integration of additional data in order to untangle causality.