Upcoming Webinars

Title Coming Soon!

November 11, 2025 at 11:30AM EST

More details coming soon!

WEBINAR DETAILS:
Webinar ID: 968 8371 7451
Password: 414559
Zoom Link: https://stanford.zoom.us/j/96883717451?pwd=3H5mAt7UaKDSYyctbmcbPtfFNe1zff.1

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. He also serves as Co-Director of the Michigan Cardiac Rehab Network and as Senior Advisor in the Michigan Value Collaborative. He and his collaborators lead federally funded research enterprises and quality improvement initiatives aimed at enhancing cardiovascular healthcare quality, outcomes, and policy.

Shu 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. Her main research focuses on causal inference and data integration, especially within the field of comparative effectiveness research in health studies. She also extensively works on methods for missing data and spatial statistics. She has served as the Principal Investigator for multiple projects funded through NCSU seed grants, industry sponsorships, NSF, NIH R01, and FDA U01.

Past Webinars

Making 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.

WEBINAR DETAILS:
Webinar ID: 968 8371 7451
Password: 414559
Zoom Link: https://stanford.zoom.us/j/96883717451?pwd=3H5mAt7UaKDSYyctbmcbPtfFNe1zff.1

Miguel Hernan

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!

You can join the webinar on Zoom here: (https://stanford.zoom.us/j/96883717451?pwd=3H5mAt7UaKDSYyctbmcbPtfFNe1zff.1) (webinar ID: 968 8371 7451). The password is 414559.

Gabriel Loewinger

BIO
Gabe Loewinger, PhD is a machine learning research scientist at the NIMH. He grew up in DC and studied neuroscience at Pitzer College in Claremont, CA. After college, he spent a year on a Watson Fellowship in Peru, Brazil, Thailand and Vietnam, and a year in Nepal on a Fulbright Fellowship, conducting research on chemical dependence. He then joined the NIH as a neuroscience researcher where he witnessed the critical role that statistical methods play in biomedical sciences. He completed his PhD in Biostatistics at Harvard where he worked with Giovanni Parmigiani and Rahul Mazumder at MIT. In his free time, he likes to train Brazilian Jiu Jitsu.

Natalie Levy

BIO
Natalie Levy, PhD, MPH is a Director of Scientific Strategy at Aetion. In this role, she provides methodological and analytic support across therapeutic areas, contributes to research to inform RWE best practices, and helps lead the Aetion Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation (CARE) Initiative. Natalie completed her doctoral training in Epidemiology at Columbia University, with a focus in epidemiologic methods and causal inference. Previously, she worked at the New York City Department of Health and Mental Hygiene where she managed the Bureau of Tuberculosis Control’s electronic surveillance registry and developed and maintained data standards for epidemiologic research, analysis, and reports. Natalie has designed and taught courses in epidemiologic study design and analysis of complex surveys and has published on a variety of topics including disease transmission, substance use, health policy evaluation, and causal inference.

Emre Kiciman

BIO
Emre Kiciman, PhD is a Senior Principal Researcher at Microsoft Research. His research interests span causal inference, machine learning, and AI’s implications for people and society. He is working to broaden the use of causal methods for decision-making across many application domains; and, in the broad area of AI’s implications for society. His projects include work at the intersection of security and machine learning. He has a strong interest in computational social science questions and social media analyses, especially that require causal understanding of phenomenon in health, mental health; issues of data bias; and understanding how new technologies affect our awareness of the world and enable new kinds of information discovery and retrieval.

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.

Recording

Elizabeth A. Stuart, Ph.D. </br> John Hopkins University

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

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