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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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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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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Dr. Coffman’s research focuses on improving methods for causal inference, specifically for continuous treatments and mediation.
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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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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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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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Dr. Hsu’s statistical research projects focus on statistical methods in observational studies and causal inference.
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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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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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Dr. Li conducts methodological research in causal inference, unmeasured confounding, missing data, mediation, Bayesian analyses and survey methods.
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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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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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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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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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Dr Rosenbaum is interested in causal inference in observatonal studies.
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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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Dr. Schaubel’s methodologic research interests mostly involve survival analysis and the analysis of recurrent event data.
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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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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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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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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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Dr. Zhao is interested in causal inference in high dimensional settings.
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Dr. Blette's research interests focus on applications and methods development for causal inference, survival analysis, clinical trials, and health policy.
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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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Dr. Roy's research interests focuses on different paradigms of data science and machine learning with expertise in causal inference, high dimensional data, and credit risk modeling.
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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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.
Dr. Westling's research focuses on developing semiparametric efficiency theory and nonparametric statistical methods in causal inference and survival analysis.
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