Centers of Excellence

CCI

Center for Causal Inference

People

People

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

Dr Roy's current methodological research interests 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, and his methodological research is motivated by challenges 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, University of Pennsylvania
Assistant Professor of Education

Dr. Chan specializes in applied educational statistics, and her research projects and interests are at the leading edge of work on statistics methods in field contexts, including scaling up interventions. 
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Donna L. Coffman, Temple University
Assistant Professor of Biostatistics

Dr. Coffman’s research focuses on improving methods for causal inference, specifically for continuous treatments and mediation. 
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Maria Cuellar, University of Pennsylvania
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.
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Sean Hennessy, University of Pennsylvania
Professor of Epidemiology and of Systems Pharmacology and Translational Therapeutics

Dr. Hennessy collaborates on studies of methods used to inform causal inferences about the health effects of medications in populations. He is Director of  the Center for Pharmacoepidemiology Research & Training.
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Daniel Hopkins, University of Pennsylvania
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. 
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Yenchih (Jesse) Hsu, University of Pennsylvania
Assistant Professor of Biostatistics

Dr. Hsu’s statistical research projects focus on statistical methods in observational studies and causal inference. 
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Marshall Joffe, University of Pennsylvania
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.

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Luke Keele, University of Pennsylvania
Associate Professor of Applied Statistics

Dr. Keele specializes in research on applied statistics.  His research in focuses on causal inference, design-based methods, matching, and instrumental variables. 
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Konrad Kording, University of Pennsylvania
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? 
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Yun Li, University of Pennsylvania
Associate Professor of Biostatistics

Dr. Li conducts methodological research in causal inference, unmeasured confounding, missing data, mediation, Bayesian analyses and survey methods. 
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Kristin Linn, University of Pennsylvania
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. 
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Qi Long, University of Pennsylvania
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.
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Elizabeth Ogburn , Johns Hopkins University
Assistant Professor of Biostatistics

Dr. Ogburn's research is in causal inference and epidemiologic methods. Broadly, she is interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met.
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Gregory Ridgeway, University of Pennsylvania
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.
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Paul Rosenbaum, University of Pennsylvania
Professor of Statistics

Dr Rosenbaum is interested in causal inference in observatonal studies.
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Douglas E. Schaubel, University of Pennsylvania
Professor of Biostatistics

Dr. Schaubel’s methodologic research interests mostly involve survival analysis and the analysis of recurrent event data.
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Alisa Stephens-Shields, University of Pennsylvania
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. 
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Eric Tchetgen Tchetgen, University of Pennsylvania
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. 
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Lyle H. Ungar, University of Pennsylvania
Professor, Computer and Information Science

Dr. Ungar'sresearch focuses on developing scalable machine learning methods for data mining and text mining, including deep learning methods for NLP, and analysis of text and images in social media to better understand the drivers of physical and mental well-being.
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Wei (Peter) Yang, University of Pennsylvania
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.  
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Qingyuan Zhao, University of Cambridge
University Lecturer of Statistics

Dr. Zhao is interested in causal inference in high dimensional settings.
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Youjin Lee, University of Pennsylvania
Postdoctoral Researcher, CCI

Dr. Lee's research interests are in statistical methodology for network data, particularly relating to causal inference. She is especially interested in developing methods that are useful and accessible to researchers and policy makers in diverse fields.
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Andrew Spieker, Vanderbilt University Medical Center
Assistant Professor of Biostatistics

Dr. Spieker's research interests are primarily centered on the integration of causal inference methodology into fields such as pharmacoepidemiologic association studies, cost-effectiveness research, and preliminary HIV vaccine trials.
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Yaoyuan (Vincent) Tan, Vertex Pharmaceuticals
Senior Biostatistician II

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.

Ted Westling, University of Massachusetts at Amherst
Assistant Professor of Mathematics and Statistics

Dr. Westling;s research focuses on developing semiparametric efficiency theory and nonparametric statistical methods in causal inference and survival analysis.
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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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