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University of Cambridge Dr. Zhao is interested in causal inference in high dimensional settings.

University of Pennsylvania 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 […]

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

University of Pennsylvania Dr Rosenbaum is interested in causal inference in observatonal studies.

University of Pennsylvania 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 […]

Johns Hopkins University 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 […]

University of Pennsylvania Dr Kording’s current focus is on causality in data science applications – how do we know how things work if we cannot randomize? 

University of Pennsylvania Dr. Keele specializes in research on applied statistics.  His research in focuses on causal inference, design-based methods, matching, and instrumental variables. 

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

Temple University Dr. Coffman’s research focuses on improving methods for causal inference, specifically for continuous treatments and mediation. 

University of Pennsylvania 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 […]

Contact Blockley Hall (Room 633)423 Guardian DrivePhiladelphia, PA 19104 Office: 215-573-4922Fax: 215-573-4865 vallejo@pennmedicine.upenn.edu

Dr. Yang’s methodological research includes causal inference, functional data analysis and dynamic risk prediction. He is also interested in collaborative research in chronic kidney disease, cardiovascular disease and pharmacoepidemiology.

Dr. Stephens Shields is an Associate Professor of Biostatistics. Her research is focused on extensions and innovative applications of causal inference approaches to enhance the design and analysis of clinical […]

As University Professor, Dr. Tchetgen Tchetgen holds joint primary appointments in the Department of Statistics and Data Science at The Wharton School and in the Department of Biostatistics, Epidemiology and […]

Dr. Schaubel’s methodologic research interests mostly involve survival analysis and the analysis of recurrent event data. His more recent methodology has been at the intersection of causal inference and survival […]

Dr. Linn’s methodological research addresses barriers to incorporating complex, high-dimensional data into models for personalized medicine and biomarker development using neuroimaging data. As a co-investigator, she provides biostatistical leadership on […]

Dr. Long’s research purposefully includes novel statistical and machine learning research and impactful biomedical research, each of which reinforces the other. Its thrust is to develop statistical and machine learning […]

Dr. Mitra’s primary research interests include the design and analysis of observational studies, causal inference, and statistical approaches for cost-effectiveness analysis. She has developed doubly robust approaches to estimation of […]

Yun Li, PhD is Associate Professor of Biostatistics in the Perelman School of Medicine at University of Pennsylvania’s Department of Biostatistics, Epidemiology and Informatics (DBEI), and a faculty member at […]

Dr. Hsu’s statistical research focuses on methods for observational studies and causal inference. He has specific interests in survival analysis, longitudinal data analysis, inverse probability treatment weighting, and the potential […]

Sean Hennessy, PharmD, PhD, uses healthcare data to generate real-world evidence about the health effects of prescription drugs. His team has studied serious health consequences of drug-drug interactions involving high-risk […]

Dr. Maria Cuellar is an Assistant Professor at the University of Pennsylvania in the Department of Criminology, Department of Statistics and Data Science, and the Department of Biostatistics, Epidemiology and […]

Nandita Mitra, PhD, Professor of Biostatistics, has been awarded the 2024 L. Adrienne Cupples Award for her outstanding contributions to research, teaching, and leadership in biostatistics and public health.

We are pleased to announce that Bryan Blette and Andrew Ying were seclected as the winners of the highly competitive ASA Biometrics Section Paper award. The awardes will present their […]

Doubly robust nonparametric instrumental variable estimators for survival outcomes Youjin Lee, Edward H Kennedy, Nandita Mitra Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal […]

We are pleased to announce that Jason Roy, CCI Co-Director, has been selected for the 2021 ASA Causality in Statistics Education Award for his work to advance Causality in Statistics […]

Job Vacancies and Immigration: Evidence from the Mariel Supply Shock L. Jason Anastasopoulos, George J. Borjas, Gavin G. Cook, and Michael Lachanski We use the Conference Board’s Help-Wanted Index (HWI) […]

Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S. Small Unmeasured confounding is a key threat […]

Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery Arman Oganisian, Nandita Mitra, Jason Roy Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these […]

Moving Toward Rigorous Evaluation of Mobile Health Interventions Kristin A. Linn Qian, Klasnja and Murphy provide an assumption that allows for unbiased estimation of treatment effects in microrandomized trials when […]

A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya Maria Cuellar and Edward H. Kennedy Current estimation methods for the probability of causation […]

A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation Arman Oganisian, Nandita Mitra, and Jason Roy Researchers are often interested in predicting outcomes, conducting clustering analysis to […]

Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions Chanmin Kim, Corwin M Zigler, Michael J Daniels, Christine Choirat, and Jason A Roy Pollutant […]

Patterns of Effects and Sensitivity Analysis for Differences-in-Differences Luke J. Keele, Dylan S. Small, Jesse Y. Hsu, and Colin B. Fogarty Applied analysts often use the differences-in-differences (DID) method to […]

Robust causal inference with continuous instruments using the local instrumental variable curve Edward H. Kennedy, Scott Lorch, and Dylan S. Small Instrumental variables are commonly used to estimate effects of […]

Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables Wang L, Tchetgen Tchetgen E. Instrumental variables (IVs) are widely used for estimating causal effects in the […]

Estimating scaled treatment effects with multiple outcomes Edward H Kennedy, Shreya Kangovi, Nandita Mitra Link In classical study designs, the aim is often to learn about the effects of a […]

Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders Elizabeth Handorf, Daniel Heitjan, Justin Bekelman, Nandita Mitra Link The analysis of observational data to determine the cost-effectiveness […]

Sensitivity analysis and power for instrumental variable studies Wang X, Jiang Y, Zhang NR, Small DS. Link In observational studies to estimate treatment effects, unmeasured confounding is often a concern. […]

Eliminating survivor bias in two-stage instrumental variable estimators. Vansteelandt S, Walter S, Tchetgen Tchetgen E. Link Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis […]

A general approach to evaluating the bias of 2‐stage instrumental variable estimators Fei Wan, Dylan Small, and Nandita Mitra Unmeasured confounding is a common concern when researchers attempt to estimate […]

Bayesian nonparametric generative models for causal inference with missing at random covariates Jason Roy, Kirsten J. Lum, Bret Zeldow , Jordan D. Dworkin, Vincent Lo Re III, and Michael J. […]

A doubly robust approach for cost–effectiveness estimation from observational data Li, Vachani, Epstein, Mitra Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is […]

A new, powerful approach to the study of effect modification in observational studies Kwonsang Lee, Dylan S. Small, Paul R. Rosenbaum Effect modification occurs when the magnitude or stability of […]

The Trend-in-Trend Research Design for Causal Inference. Ji X, Small DS, Leonard CE, Hennessy S. Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called […]

Constructed Second Control Groups and Attenuation of Unmeasured Biases Samuel D. Pimentel, Dylan S. Small & Paul R. Rosenbaum The informal folklore of observational studies claims that if an irrelevant […]

An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies. Rosenbaum PR, Small DS In a sensitivity analysis in an observational study with a binary outcome, is it better to […]

A framework for Bayesian nonparametric inference for causal effects of mediation. Kim C, Daniels MJ, Marcus BH, Roy JA We propose a Bayesian non-parametric (BNP) framework for estimating causal effects […]

A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Roy J, Lum KJ, Daniels MJ. Marginal structural models (MSMs) are a general […]

Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available. Ertefaie A, Small D, Flory J, Hennessy S. Instrumental variable (IV) methods […]

An evaluation of bias in propensity score-adjusted non-linear regression models Fei Wan, Nandita Mitra Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment […]