Center for Causal Discovery

The Center for Causal Discovery (CCD) aims to help discover valid, novel, and significant causal relationships in big biomedical data that lead to new insights in health and disease. Many statistical methods cannot accommodate the tens of thousands of variables that may appear in these datasets. In addition, as anyone who’s taken an introductory statistics class knows, correlation does not equal causation. However, algorithms to discover causal relationships from observational data do exist, and the Center for Causal Discovery is working to improve the ability of existing and new discovery algorithms to handle tens and hundreds of thousands of variables through advances in: