Daniel received his PhD from Colorado State University where he developed statistical methods for studying the relationship between maternal exposures to air pollution and birth and children’s health outcomes. His research interests include: statistical machine learning, functional regression, variable selection, effect heterogeneity, and causal inference, with an application to understanding the health effects of environmental exposures.
My research focuses on problems at the interface of the mathematical sciences and public policy. My primary methodological work is in causal inference, focusing on questions of causal “data fusion,” in which observational and experimental data sources are merged. I am also interested in political science and the analysis of American elections. I hold a PhD in Statistics from Stanford University.
Dafne got her bachelor’s and master’s degree in Statistics in Padua, Italy. She is currently a visiting Ph.D. student at the Harvard T.H. Chan School of Public Health. Her interests are Bayesian nonparametric models and causal inference.