Stephen R. Cole works to build accurate and impactful knowledge, particularly population-health (epidemiologic) knowledge.
Professor Cole is interested in study designs and analyses that accurately estimate parameters of central interest to population-health scientists, such as risk. These study designs include randomized experiments, pseudoexperiments (i.e., observational studies) and thought-experiments (e.g., simulation studies). Substantively, Dr. Cole is interested in infectious diseases, primarily HIV, and cancer.
Jennifer Hill develops and evaluates methods that help answer causal questions vital to policy research and scientific development. Her ast work focused on situations in which it is difficult or impossible to perform traditional randomized experiments, or when even seemingly pristine study designs are complicated by grouped structures or missing data. Most recent work focuses on Bayesian nonparametric methods that allow for flexible estimation of causal models and are less time-consuming and more precise than competing methods (e.g. propensity score approaches). These approaches intersect with other causal inference topics such as common support violations, sensitivity analysis, and estimation of heterogeneous effects. Hill has published in a variety of leading journals including Journal of the American Statistical Association, Statistical Science, American Political Science Review, American Journal of Public Health, and Developmental Psychology. She earned her PhD in Statistics at Harvard University in 2000 and completed a post-doctoral fellowship in Child and Family Policy at Columbia University’s School of Social Work in 2002. Hill is current the Director of the Center for Practice and Research at the Intersection of Information, Society, and Methodology (PRIISM) and Co-Director of and the Master’s of Science Program in Applied Statistics for Social Science Research (A3SR).
Luke Keele (Ph.D., University of North Carolina, Chapel Hill, 2003) is currently an Associate Professor at the University of Pennsylvania with joint appointments in Surgery and Biostatistics. Professor Keele specializes in research on applied statistics with a focus on causal inference, design-based methods, matching, natural experiments and instrumental variables. He also conducts research on topics in educational program evaluation, election administration, and health services research. He has published articles in the Journal of the American Statistical Association, Annals of Applied Statistics, Journal of the Royal Statistical Society, Series A, The American Statistician, American Political Science Review, Political Analysis, and Psychological Methods.
Ilya Shpitser is a John C. Malone Assistant Professor of Computer Science, Johns Hopkins University. His primary area of interest is causal and probabilistic inference, graphical models, missing data, dependent data, and algorithmic fairness. The primary application area for Dr. Shpitser’s work is healthcare, medicine, and public health.
Dylan Small, PhD is the Class of 1965 Wharton Professor in the Department of Statistics at the Wharton School at the University of Pennsylvania. Dr. Small received his PhD in Statistics from Stanford University and his AB in Mathematics from Harvard University. Dr. Small’s research interests include causal inference and applications of statistics to health and public policy. He was the founding Editor of the journal Observational Studies and continues to serve on the editorial board of Observational Studies as well as the Journal of Casual Inference and several other statistical journals.