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Computational Causal Discovery Laboratory, Center for Health Informatics and Bioinformatics, NYU Langone Medical Center

550 1st Avenue
New York, NY 10016
Research Institutes
550 1st Avenue
New York, NY 10016
ccdlab.org

Description

Discovery of causal mechanisms from data is a fundamental problem of several computational disciplines including computer science, statistics, and applied mathematics. Obtaining data from randomized controlled experiments, while being fundamental for discovery of causality, is very expensive and can often be infeasible and unethical. On the other hand, non-randomized observational (e.g., case-control, case-series, time-series) data that is collected without experimental interference of the values of variables is highly abundant in the public domain and otherwise can be often collected cheaply. Over the last 20 years, many sound algorithms have been proposed that can leverage observational data to infer causal relations.

Source: http://ccdlab.org

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