Causal Inference Methods

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 large data sets like Medicare.

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Air Pollution, Climate Change, & 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 US.

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Comparative Effectiveness Research in Cancer

In close collaboration with researchers and clinicians at the Dana Farber Cancer Institute and at the Harvard Medical School, Dr. Dominici develops Bayesian methods in causal inference for Comparative Effectiveness Research (CER). She applies these methods to administrative data to address key CER questions in the treatment of Glioblastoma and other cancers.

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