Daniela Golinelli, PhD, is a Research Professor at Penn Nursing in the Center for Health Outcomes and Policy Research.
Dr. Golinelli’s methodological research focuses on two primary areas: Methods for causal inference in observational studies, including the use of machine learning algorithms to estimate propensity score weights, and sampling hard-to-reach populations by developing sophisticated sampling designs to obtain large probability samples of homeless women, men, and youth from shelters, drop-ins, meal lines, and streets.
Her current applied research focuses on the role of nurses in mitigating health outcome disparities and conducting evaluations of nursing programs in both experimental and quasi-experimental settings.
Dr. Golinelli completed her undergraduate studies in Italy at Bocconi University with a degree in Economics and Statistics, and earned a PhD in Statistics from the University of Washington.
