Public Policy and Social Sciences
Statistical methods are now a primary tools for the collection and analysis of data to inform the education, policy, and social sciences. From questionnaire development to the selection of probability samples to the design of social experiments, statisticians at Carnegie Mellon collaborate in the collection of social science data. Faculty and students regularly work with others to develop new methods for analyzing these data and they apply up-to-date methods for drawing inferences from diverse social science data sources ranging from large scale sample surveys to social networks, to educational experiments. A number of statistics graduate students work directly in joint programs bringing statistics to bear on problems in education and public policy.
Department Members with this Research Interest
Estella Loomis McCandless Assistant Professor of Statistics and Public Policy
Leonard J. Savage University Professor of Statistics and Social Sciences, Emeritus
- Causal Inference Working Group