Speakers - 2026 Penn Causal Inference Summer Institute. Online registration by Cvent

Eric J. Tchetgen Tchetgen is The University Professor, Professor of Biostatistics at the Perelman School of Medicine and Professor of Statistics and Data Science at The Wharton School at the University of Pennsylvania. He co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences. He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer's Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space. Professor Tchetgen Tchetgen is a 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Medicine and Public Health.

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 cost-effectiveness measures, nonparametric influence function based instrumental variable estimators for censored outcomes, and model-based sensitivity analysis approaches. She collaborates with investigators in oncology, health policy, and health economics and has been lead statistician on studies of the comparative and cost-effectiveness of competing therapies using large observational databases.

Dr. Mitra is the Co-Director of the Center for Causal Inference. In addition, she is Chair of the Budget & Finance Committee of the International Biometrics Society, Chair of the American Statistical Association Statistics in Epidemiology Section, and Secretary of the Society for Causal Inference. She was recently awarded the American Statistical Association Mentorship Award. She is Editor-in-Chief of Observational Studies and Fellow of the American Statistical Association.

Dylan Small is a statistician specializing in observational studies, causal inference and applications to the health and social sciences. Dr. Small is the Universal Furniture Professor and Department Chair in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He joined the University of Pennsylvania in 2002 after obtaining his Ph.D. in statistics from Stanford University in 1997. Dr. Small is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Dr. Small was the founding editor of the journal Observational Studies, the first journal to focus on observational studies.

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 trials. She also works in the development of patient-reported outcomes to inform population-appropriate trial endpoints. Dr. Stephens Shields provides statistical leadership to clinical trials and observational studies in numerous clinical areas, including pediatrics, chronic pain, urology, and infectious disease.

Her current collaborations include Handoffs and Transitions in Critical Care – Understanding Scalability, a stepped wedge cluster randomized trial aiming to improve patient outcomes through implementing tailored protocols for transitioning patients from surgery to intensive care, and the American Heart Association-awarded Behavioral Economics to Transform Trial Enrollment Representativeness (BETTER) Center, which is evaluating methods to increase diverse participation in clinical trials. Dr Stephens Shields directs the Biostatistics and Data Science Core of the Penn Center for AIDS Research.

Dr. Stephens Shields currently serves as an associate editor for Biostatistics and the Annals of Internal Medicine and previously held editorial roles with Pharmacoepidemiology and Drug Safety and Epidemiologic Methods.

Nicholas J. Seewald, PhD is an Assistant Professor in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine at the University of Pennsylvania. Previously, Dr. Seewald served as a postdoctoral fellow at the Johns Hopkins Bloomberg School of Public Health. Dr. Seewald earned a BS in Mathematics with Life Science from the University of Notre Dame (2013), and then continued his academic career at the University of Michigan where he obtained an MS in Biostatistics (2015), an MA in Statistics (2018), and a PhD in Statistics (2021).

Dr. Seewald’s methodological research is primarily related to causal inference using complex repeated measures data, using both experimental and non-experimental approaches, particularly difference-in-differences and the design and analysis of sequential multiple-assignment randomized trials (SMARTs). His work is motivated by problems across a wide array of applications, including oncology, substance use and related policy, chronic disease, and mobile health, and spans the entire investigative process from formulating a research question through study design and data analysis. He is deeply interested in building tools to address important statistical issues in a way that is accessible and understandable to applied researchers.

Dr. Enrique F. Schisterman is Perelman Professor and Chair of the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine. He is an elected member of the National Academy of Medicine in recognition of his contributions to reproductive epidemiology and epidemiological methods.

Dr. Schisterman’s career has been defined by a holistic approach that integrates methodological innovation with substantive investigation. His complementary training in epidemiology and statistics has enabled him to both develop new analytical methods and apply them to understand the etiology of reproductive health outcomes. This dual focus has produced advances in both how we study fertility and pregnancy, and in what interventions can improve reproductive outcomes.

His clinical research includes several landmark randomized trials. The Effects of Aspirin on Gestation and Reproduction (EAGeR) Trial demonstrated that low-dose aspirin increases live birth rates in women with prior pregnancy loss and chronic inflammation. The Folic Acid and Zinc Supplementation Trial (FAZST) provided definitive evidence against the utility of these supplements for male fertility, highlighting the need for rigorous evaluation of commonly used interventions. The BioCycle Study, a prospective observational study, established foundational knowledge about endogenous hormone patterns and oxidative stress biomarkers across the menstrual cycle.

In parallel, Dr. Schisterman has made substantial methodological contributions to epidemiology. His work addresses fundamental challenges in exposure assessment using biomarkers, including measurement error, detection limits, and pooling strategies. He has advanced understanding of causal inference, particularly regarding collinearity, confounding, and over-adjustment in observational studies. His statistical innovations have provided tools for handling left-censored data, time-varying exposures, and complex longitudinal designs that are now widely applied across the field.

Before joining Penn in 2021, Dr. Schisterman spent almost two decades at the National Institutes of Health, where he served as Senior Investigator and Chief of the Epidemiology Branch in the Division of Intramural Population Health Research at the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Dr. Schisterman currently serves as Principal Investigator on both the APPLE Trial and a PCORI-funded grant developing transportability methods for randomized trials, continuing his work at the intersection of clinical intervention research and methodological development. He has published more than 450 peer-reviewed papers and serves as Editor-in-Chief of the American Journal of Epidemiology. He has received numerous honors including the Outstanding Contributions to Epidemiology Award for Methods Development from the American College of Epidemiology, the Excellence in Education Award from the Society of Epidemiologic Research, and the SPER Mentoring Award. He is an elected member of the American Epidemiological Society and has served as president of both the Society for Epidemiologic Research and the Society for Pediatric and Perinatal Epidemiologic Research.

Dr. Caniglia is a perinatal and HIV epidemiologist who works to improve health outcomes among pregnant people and their children, and among people with HIV. Her work utilizes methods for causal inference to identify optimal treatment and prevention strategies in these populations. She received a K01 award from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) to estimate the effects of micronutrient supplementation strategies during pregnancy on adverse birth outcomes, to identify barriers to supplementation, and to pilot an intervention to provide supplementation at antenatal clinics in Botswana. Additional current and future projects include evaluating the effects of lifestyle, pharmaceutical, and health services interventions to improve neonatal and maternal outcomes and disparities in these outcomes. Dr. Caniglia collaborates broadly on projects related to causal inference; HIV; and reproductive, perinatal, and pediatric epidemiology. She is passionate about teaching the next generation of epidemiologists.

Dr. Spieker is an Associate Professor in the Department of Biostatistics at Vanderbilt University Medical Center, and the Director of Graduate Studies for Vanderbilt University’s Biostatistics MS and PhD programs. His methodological work centers on causal methods for various applications including studies of mobile health interventions. His collaborations are largely in pediatrics, internal medicine, and health policy. He is the Book Review Editor for Biometrics and Co-Editor-in-Chief of Observational Studies.

Luke Keele, PhD, is an Associate Professor of Applied Statistics in the Department of Surgery at the University of Pennsylvania. In this capacity, he conducts research on statistical methods for causal inference and program evaluation, health services research, and the social sciences. He is particularly interested in matching methods, instrumental variables, randomization inference, and regression discontinuity designs.

He has published work in journals such as the Journal of the American Statistical Association, the Annals of Applied Statistics, Psychological Methods, Statistical Science, the Journal of the Royal Statistical Society, Series A, Statistics in Medicine, the American Journal of Political Science, and the American Political Science Review.

Chan Park is an assistant professor in the Department of Statistics at the University of Illinois Urbana-Champaign.

He was a postdoctoral researcher in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania, mentored by Professor Eric J. Tchetgen Tchetgen. In May 2022, he received his Ph.D. in Statistics from the University of Wisconsin-Madison, where he was advised by Professor Hyunseung Kang. Prior to joining the Ph.D. program in July 2017, he worked as a statistician for two and a half years at the Central Bank of Korea. Chan received a B.S. in Statistics from Seoul National University.

Chan’s research broadly focuses on: (a) causal inference under interference and non-i.i.d. settings; (b) causal inference under unmeasured confounding; and (c) optimal treatment regimes and policy learning. A common theme in his research is to use non/semiparametric theory and optimization methods to develop efficient and robust estimators of causal quantities in (a)-(c).

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 drugs including anticoagulants, antidiabetes agents and antiplatelet agents. His research has produced crucial knowledge about the cardiovascular safety of many widely-used drugs for mental health conditions including ADHD, depression and schizophrenia. He also evaluated an early approach to using medical insurance data to improve prescribing, finding it ineffective despite its federal mandate. This contributed to the omission of drug utilization review programs from Medicare Part D. He co-led a pair of studies demonstrating the effectiveness and safety of the SA14-14-2 vaccine for Japanese encephalitis (JE), which subsequently led to the immunization of millions of children per year in many populous countries including Cambodia, India, Malaysia, Nepal, Sri Lanka and Thailand. Use of that vaccine has been credited with reducing the incidence of JE. He co-developed the instrumented difference-in-differences design for study of the effects of rapidly increasing or declining exposures. He was also the senior author of a citizen petition to the U.S. Food and Drug Administration that led to re-labeling of metformin, the best-proven oral drug for diabetes, to permit its use in the millions of persons with both diabetes and mild to moderate renal insufficiency.

Dr. Hennessy is a past scientific chair and past president of the International Society for Pharmacoepidemiology, past chair of NIH’s Health Services Quality and Effectiveness study section, and has served on the FDA’s Drug Safety and Risk Management Advisory Committee and the board of directors of the American Society for Clinical Pharmacology and Therapeutics. He is a co-editor of the books Pharmacoepidemiology, 6th edition and Textbook of Pharmacoepidemiology, Third edition.

Dr. Miano is a critical care pharmacist and epidemiologist whose work aims to reduce harm from adverse drug events in acutely ill patients. His research focuses on understanding the determinants and impacts of nephrotoxicity (the syndrome of rapid kidney function loss resulting from drug toxicity), and the health impacts of drug-drug interactions (DDI). Nephrotoxicity is a major, modifiable cause of acute kidney injury (AKI) in hospitalized patients, especially in the setting of polypharmacy, where numerous drug combinations can interact to increase toxicity risk. Nephrotoxic AKI accounts for nearly one third of AKI events in hospitalized patients, and has severe adverse consequences, including increased risk of death and a higher risk of chronic kidney disease onset and progression. To address these public health problems, he leads a translational, acute care pharmacoepidemiology research program that integrates information from observational causal inference studies, prospective molecular epidemiologic studies, and randomized clinical trials to examine the comparative safety of nephrotoxic drugs and to understand underlying molecular mechanisms of toxicity.

His work has advanced our understanding of the limitations of current phenotyping methods for nephrotoxic AKI. Serum creatinine, the standard biomarker of nephrotoxicity, is a kidney function biomarker that has poor sensitivity and specificity for kidney parenchymal injury. In a seminal paper, Dr. Miano found that commonly prescribed antibiotics, when used in combination, can lead to false elevations in creatinine, so called pseudo-nephrotoxicity. The study further found that cystatin C, a novel kidney function biomarker, can be used to accurately detect changes in kidney function during antibiotic treatment. This work questions the results of dozens of prior creatinine-based studies and stands to establish a new paradigm for evaluating drug-associated AKI. His lab was also the first to show that kidney disease strongly modifies the severity of drug-drug interactions that are mediated by inhibition of hepatic metabolism, a finding that has mechanistic implications for dozens of drug combinations commonly encountered in clinical practice.

Ilya Shpitser, a John C. Malone Associate Professor in the Department of Computer Science, works on causal and semi-parametric inference, missing data, and algorithmic fairness – ubiquitous data complications that may arise in datasets of all types, such as those obtained from social networks, electronic medical records, criminal justice databases, or longitudinal studies.

His methods yield principled approaches to detecting and addressing disparities and algorithmic bias, understanding causal pathways, and making appropriate causal inferences in settings where observations are systematically censored, unobserved confounders are present, observed realizations are correlated, or the problem is sufficiently complex that simple parametric approaches are unrealistic. The goal of his work is to allow inferences about cause-effect relationships to be made from complex, high-dimensional observational data, which is a crucial task in the empirical sciences and rational decision-making.

Recent applications of Shpitser’s work include analysis of adherence in HIV patients, investigating the association between highly active antiretroviral therapy in pregnant women and birth defects, and developing predictive models and dimension reduction strategies using oncology data. His research also examines corrections for discriminatory bias in criminal justice data and learning predictors and causes of adverse outcomes in cardiac surgery patients.

In 2017, Shpitser was honored with the Causality in Statistics Education Award by the American Statistical Association for his annual Johns Hopkins course on causal inference for advanced undergraduate and graduate students in data science allied disciplines (computer science, statistics, public health, social science, and economics). Before joining Johns Hopkins, he was a lecturer in Statistics at the University of Southampton, Southampton, UK.

Shpitser serves as associate editor of the Journal of Causal Inference. He is a member of the research advisory board at Arnold Ventures, a limited liability company for research and evidence-based methods, and a senior program committee member for the International Conference on Machine Learning (ICML), Neural Information Processing Systems (NeurIPS). Additionally, he reviews articles for Uncertainty in Artificial Intelligence (UAI), the International Joint Conference on Artificial Intelligence (IJCAI), European Conference on Artificial Intelligence (ECAI), and the Journal of Machine Learning Research, among others.

Shpitser has authored numerous papers and several book chapters, including for the Handbook of Graphical Models (Chapman & Hall, 2018). He co-organized a tutorial on graphical methods for identification at the 2019 Atlantic Causal Inference Conference. Among his invited presentations were the 2019 Harvard Applied Statistics Workshop, the 2019 Institute for Computational and Experimental Research in Mathematics (ICERM) Workshop on Models and Machine Learning for Causal Inference and Decision Making in Health Research, the 2018 Uncertainty in Artificial Intelligence (UAI) causal inference workshop, and the 2018 Defense Advanced Research Projects Agency (DARPA) Ground Truth program.

He received his BA in Computer Science and Mathematics (1999) from the University of California, Berkeley, and his MS (2004) and Ph.D. (2008) in Computer Science from the University of California, Los Angeles. Shpitser’s postdoctoral fellowships include the UCLA’s Department of Computer Science and Harvard University’s Department.

Dr. Arman Oganisian is an assistant professor of biostatistics at Brown University. His methodological research centers around developing Bayesian nonparametric methods for causal inference, with a focus on sequential treatment strategies with incomplete information. His research is funded by contracts from the Patient-Centered Outcomes Research Institute (PCORI) and recently received the Salomon Faculty Research Award by Brown University. He received his PhD in biostatistics from the University of Pennsylvania.

AlexLevisis an Assistant Professor of Biostatistics in the Center for Causal Inference and the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania. Before joining Penn, he obtained his PhD in Biostatistics at Harvard University in 2022, and subsequently was a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University. Alex's expertise lies in statistical methods development related to causal inference, missing data, non- and semi-parametrics, machine learning, and the use of administrative and electronic health record data for comparative effectiveness research. He is interested in a broad range of application areas spanning the biomedical sciences, focusing recently on problems in surgery, experimental neuroscience, and mental health.

https://www.awlevis.com/

My research focuses on developing robust and efficient methodologies that minimize reliance on restrictive modeling assumptions. I am particularly interested in creating methods that integrate machine learning to ensure data-adaptive approaches while maintaining valid statistical inference. My specific areas of expertise include causal inference, individualized treatment strategies, instrumental variable analyses, high-dimensional data analysis, post-selection inference, and survival analysis.