Upcoming Webinars
Death and Taxes: Taking Causal Inference into the Real World
October 20, 2026 at 12:00PM EST
Statisticians develop methods with the hope that they will ultimately inform decisions and improve the world around us. But taking causal inference into the real world requires more than choosing the right estimator: it requires identifying the question that matters, engaging deeply with its context, and communicating statistical reasoning to very different audiences. Read more
Nandita Mitra
Guanbo Wang
Nandita Mitra is Professor of Biostatistics at the Perelman School of Medicine and Professor of Statistics and Data Science at Wharton at the University of Pennsylvania. She is also the co-director of the Penn Center for Causal Inference and former Chair of the Graduate Group in Epidemiology and Biostatistics at Penn. She received her BA in Mathematics from Brown University, MA in Biostatistics from the University of California, Berkeley, PhD in Biostatistics from Columbia University, and completed a postdoctoral fellowship at Harvard. Read more
Guanbo Wang is an Assistant Professor at The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth. His primary research focuses on causal inference in the context of clinical trial, data integration, survival analysis, and heterogeneity of treatment effect.
Machine Learning & Nonparametric Efficiency in Causal Inference
October 30, 2026 at 12:00PM EST
This short course covers the basics of efficient nonparametric estimation in causal inference, including estimating equations, TMLE, and double machine learning. It considers nonparametric efficiency bounds for causal estimands, and efficient bias-corrected estimators based on influence functions. Importantly, these estimators yield fast rates of convergence and normal limiting distributions, even in complex nonparametric models where nuisance functions (e.g., propensity scores) are estimated with modern machine learning tools. The estimators are often doubly robust. Background in mathematical statistics is useful but not required. The workshop covers both theory and application, including R code for implementing various methods.
Edward Kennedy
Edward Kennedy is a professor of Statistics & Data Science at Carnegie Mellon University. He joined the department after graduating with a PhD in biostatistics from the University of Pennsylvania. Edward’s research interests lie at the intersection of causal inference, machine learning, and nonparametric theory, especially in settings involving high-dimensional and otherwise complex data. His applied work focuses on problems in criminal justice, health services, medicine, and public policy. Edward is a recipient of the Mortimer Spiegelman Award for outstanding contributions to health statistics, the NSF CAREER Award, and the Thomas Ten Have Award for exceptional research in causal inference.
Past Webinars
Funding Your Work: Thinking Beyond the NIH
November 11, 2025 at 11:30AM EST
In this session, our panelists will discuss diverse funding opportunities for causal inference and related research. Learn about foundation grants, industry partnerships, federal agencies beyond the NIH, and other sources that support innovative methodological and applied work.
Michael Thompson
Shu Yang
Michael Thompson, PhD, is the Director of Analytic Strategy for the Center for Healthcare Outcomes and Policy and Associate Professor of Cardiac Surgery at Michigan Medicine with joint appointments in the Department of Health Management and Policy and Department of Epidemiology at the University of Michigan School of Public Health.
MoreShu Yang is a Professor of Statistics at North Carolina State University. She earned her Ph.D. in Applied Mathematics and Statistics from Iowa State University and completed postdoctoral training at Harvard T.H. Chan School of Public Health.
MoreMaking decisions is hard but making decisions without data is much harder: How causal inference research helped governments during the last pandemic
October 07, 2025 at 11:30AM EST
The first question a decision maker asks is “Do we have a problem?”; the second one is “How do we handle the problem?”. Answering the first question requires descriptive studies; answering the second one requires causal studies. This talk describes examples of how this process worked in the real world during the last pandemic. It is partly based on my experience as an embedded researcher in a government agency. Some take-home messages are: conducting good descriptive studies is difficult but indispensable; actionable causal inference can sometimes rely on randomized trials but will often have to rely on observational emulations of trials; sometimes the causal questions are so complex that only mathematical models will help decision makers; and researchers are usually not qualified to tell decision makers which decisions they should make.
Miguel Hernán is the Director of CAUSALab, the Kolokotrones Professor of Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health, and faculty at the Harvard-MIT Division of Health Sciences and Technology. He and his collaborators repurpose real world data into evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. This work has contributed to shape health research methodology worldwide. Read more
Roundtable Panel – Exploring Career Paths in Pharma, Government, and Technology
April 15, 2025 @ 11:30am – 12;45PM EST
Dear SCI Community,
We are pleased to announce the second webinar of the SCI-OCIS Special Webinar Series. This webinar will bring together a diverse group of experts specializing in causal inference across various industries. It is a unique opportunity to explore real-world applications of causal inference methods, gain valuable insights, and expand your professional network.
🎤 Webinar: Roundtable Panel – Exploring Career Paths in Pharma, Government, and Technology
Guest speakers: Gabriel Loewinger, PhD (NIH), Emre Kiciman, PhD (Microsoft), Natalie Levy, PhD (Aetion)
📅 Date: Tuesday, April 15
⏰ Time: 11:30 AM – 12:45 PM ET
We look forward to your participation!
Webinar Series on Topics in Causal Inference: Lessons in "causality" from National Academies consensus panels
February 19, 2025 @ 12:00 PM EST
Event Description:
Quantitative researchers working in causal inference generally have a broadly common understanding about what we mean by “causal inference,” at least with respect to estimating causal effects. Many statistical methods have been developed to estimate causal effects in individual studies, and there is a growing literature on methods for combining (or “integrating”) multiple data sources together. However, it is unclear how these advances and frameworks fit in terms of broader discussions of “causality” in science, especially for broad scientific questions that require synthesis of a wide variety of types of evidence, ranging from biological mechanistic knowledge to narrow randomized experiments to large-scale non-experimental studies, and even medical case histories. This talk will discuss lessons learned about “causality” from serving on National Academies panels, in particular one assessing a framework for “causality” used by the Environmental Protection Agency to establish potential links between exposures and health and ecological outcomes, and another that aimed to assess the literature on possible links between antimalarial exposure and long-term psychiatric symptoms among Veterans. The talk will describe the scientific contexts and lessons for us as statisticians to ensure our work is relevant and useful for such broad scientific questions.
Elizabeth A. Stuart, Ph.D. John Hopkins University
Elizabeth A. Stuart, Ph.D. is the Frank Hurley and Catharine Dorrier Chair and Bloomberg Professor of American Health in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with joint appointments in the Department of Mental Health and the Department of Health Policy and Management. She was previously Executive Vice Dean for Academic Affairs at the School. She received her PhD in Statistics from Harvard University in 2004. Her research interests are in design and analysis approaches for estimating causal effects in experimental and non-experimental studies, including questions around the external validity of randomized trials and the internal validity of non-experimental studies, as well as methods for combining data sources to assess treatment effect heterogeneity and methods for evidence synthesis. Read more