Research

Dr. Dominici’s research focuses on:

Causal inference

Dr. Dominici develops novel statistical methods for the analysis of large observational data and in particular large administrative databases such as Medicare and SEER-Medicare. Dr. Dominici is interested in developing new methods in Bayesian causal inference to overcome the challenges inherent in big data sets like Medicare. Dr. Dominici also co-leads the Harvard Data Science Initiative’s Causal Inference Program, that brings researchers from around the world to Harvard to discuss current research and developments in the field.

Air pollution, climate change, and health

Through rigorous statistical analyses of terabytes of data, Dr. Dominici’s team has provided the scientific community and policy makers with robust evidence on the adverse health effects of air pollution, noise pollution, and climate change. Her studies have directly and routinely impacted air quality policy, leading to more stringent ambient air quality standards in the U.S.