Centers of Excellence


Center for Causal Inference



Nandita Mitra, Co-Director, University of Pennsylvania
Professor of Biostatistics, University of Pennsylvania

Nandita Mitra, PhD is Professor and Vice Chair of Faculty Professional Development in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Her primary research interests include propensity score and instrumental variable methods for observational data, causal inference, health economics, and statistical genetics with applications in cancer outcomes and health policy. 

Jason Roy, Co-Director, Rutgers University
Professor of Biostatistics, Rutgers School of Public Health

Dr Roy's current methodological research interests broadly center on developing Bayesian non-parametric methods for causal inference. This includes methods for causal mediation, treatment-effect heterogeneity, and optimal treatment strategies. He is also interested in developing scalable algorithms for big data in this space. His methodological research is largely motivated by challenges that come from analyzing data from large healthcare databases.

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Dylan Small, Co-Director, University of Pennsylvania
Professor of Statistics, University of Pennslyvania

Dr Small is interested in the design and analysis of observational studies, randomized experiments with noncompliance, and applications of causal inference.

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Wendy Chan
Assistant Professor of Education
Maria Cuellar
Assistant Professor of Criminology

Dr. Cuellar's research is at the intersection of statistics and the law. She examines the use of statistical evidence in legal cases by focusing on two types of claims: claims of causal attribution and statistical claims in forensic science.


Sean Hennessy
Professor of Epidemiology

Dr. Hennessy collaborates on studies of methods used to inform causal inferences about the health effects of medications in populations. He is Director of Center for Pharmacoepidemiology Research & Training.


Daniel Hopkins
Associate Professor of Political Science

Dr. Hopkins' research seeks to make causal inferences about political behavior, and he has conducted and analyzed numerous field and survey experiments. 

Yenchih (Jesse) Hsu
Assistant Professor of Biostatistics

Dr. Hsu’s statistical research projects focus on statistical methods in observational studies and causal inference. 


Marshall Joffe
Professor of Biostatistics

Dr Joffe's methodological interests include confounding by variables affected by treatment, the effects of noncompliance, sensitivity of inference to assumptions about temporal ordering of variables, confounding by indication, dealing with unmeasured confounders, and observational assessment of screening efficacy.

Luke Keele

Professor Keele specializes in research on applied statistics.  His research in focuses on causal inference, design-based methods, matching, 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.

Konrad Kording
PIK (Penn Integrates Knowledge) Professor

Dr Kording's current focus is on causality in data science applications - how do we know how things work if we cannot randomize? 


Kristin Linn
Assistant Professor of Biostatistics

Dr. Linn is interested in the design and analysis of sequentially randomized trials focusing on health incentives, behavioral economics, and the management of chronic illnesses. She is particularly interested in estimating individualized dynamic interventions that improve long-term patient outcomes. 

Qi Long
Professor of Biostatistics

The thrust of Dr. Long's research is to advance statistical methodology and data analytics in medicine and public health with keen interests in precision medicine and implementation science and in big biomedical data including -omics, electronic health records, and mHealth data.


Gregory Ridgeway
Associate Professor of Criminology

Dr. Ridgeway has developed methodologies for estimating propensity scores using machine learning methods. He has conducted a variety of causal analyses in crime and justice applications including racial profiling, police shootings, and justice program evaluations.

Paul Rosenbaum
Professor of Statistics

Dr Rosenbaum is interested in causal inference in observatonal studies.

Alisa Stephens-Shields
Assistant Professor of Biostatistics

Dr. Stephens' research interests include clinical trials, in particular cluster-randomized trials, longitudinal data analysis, and causal inference with an emphasis on semiparametric methods. 


Eric Tchetgen Tchetgen
Luddy Family President’s Distinguished Professor, Professor of Statistics

Dr Tchetgen Tchetgen's research is in semi-parametric efficiency theory with application to causal inference, missing data problems, statistical genetics and mixed model theory. 


Wei (Peter) Yang
Associate Professor of Biostatistics

Dr. Yang’s methodological research includes causal inference, functional data analysis and joint modeling.  He is also interested in collaborative research in nephrology and pharmacoepidemiology.  


Ted Westling
Center for Causal Inference, University of Pennsylvania

Ted is interested in developing semiparametric efficiency theory and nonparametric statistical methods in causal inference and survival analysis. His dissertation work was at the intersection of targeted learning and shape-constrained estimation. He developed general theory for nonparametric inference on monotone functions, and used this theory to study a variety of problems in causal inference and survival analysis with observational data. Website

Qingyuan Zhao
Department of Statistics, University of Pennslyvania

Dr Zhao is interested in causal inference in high dimensional settings. Website

Yaoyuan (Vincent) Tan
Center for Causal Inference, Rutgers School of Public Health

Vincent is interested in developing robust Bayesian methods for a variety of causal inference problems. His dissertation work included extensions of BART and doubly robust Bayesian estimation.

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About CCI

The Center for Causal Inference (CCI) is a research center that is operating under a partnership between Penn’s Center for Clinical Epidemiology and Biostatistics (CCEB), the Department of Biostatistics and Epidemiology, Rutgers School of Public Health, and Penn’s Wharton School. The mission of the CCI is to be a leading center for research and training in the development and application of causal inference theory and methods.

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