Workshop Overview: This workshop will bring together researchers from a variety of institutions who work in the field of causal inference and modeling approaches for networks with applications to public health. Traditional assumptions in causal inference require that the exposure of one individual does not affect the outcome of another individual. However, this assumption is often dubious in applications to infectious diseases, educational interventions, and other cases where the dependence structures between individuals can affect outcomes. In many cases, consideration of a study population’s network structure leads to an improved evaluation of the impact of interventions by understanding important spillover effects. The goal of this workshop is to learn from cutting-edge research in networks and understand how this research can be implemented for public health practitioners.
- Date: Friday, March 10th 2023
- Time: 2:00pm – 5:00pm ET
- Location: This is a virtual meeting. A Zoom meeting link will be provided upon registration.
- Registration: Please use this link to register and complete a short survey.
- Cost: Free. Supported by The Avenir Award with Institutional Development Award Number DP2DA046856 from the National Institute on Drug Abuse.
This workshop is open to all, but a background in public health, epidemiology or biostatistics would be beneficial. If you are interested in participating, please join us! If you have questions or would like more information, please contact Dr. Ashley Buchanan (email@example.com).
- 2:00 – 2:05pm Welcome and Introductions (Dr. Ashley Buchanan)
- 2:05 – 2:35pm Crash-course on evaluating randomized experiments under interference on networks (Dr. Laura Forastiere)
- 2:35 – 2:40pm Break
- 2:40 – 3:10pm Introduction to peer influence and homophily with network models (Dr. Samrachana Adhikari)
- 3:10 – 3:15 Break
- 3:15 – 3:45pm Lighting talks on methods and design considerations for assessing spillover in networks (URI Avenir Research Team with Dr. Ashley Buchanan, Gabrielle Lemire, and Ke Zhang)
- 3:45 – 3:50pm Break
- 3:50 – 4:20pm Semiparametric causal inference for social network data (Dr. Elizabeth Ogburn)
- 4:20 – 4:50pm Breakout Rooms with speakers: Q&A Discussion and Coding Demos
- 4:50 – 5:00pm: Moderated discussion and Closing remarks
Samrachana Adhikari, PhD, is an Assistant Professor of Biostatistics in the Department of Population Health, NYU School of Medicine. Her research interests lie in developing and implementing statistical and machine learning tools to solve problems motivated by real-world applications in medicine, global health and education. Her methodological work has focused on statistical social network analysis, penalized regression for longitudinal data, and Bayesian causal inference.
Ashley Buchanan, DrPH, is an Associate Professor of Biostatistics in the Department of Pharmacy Practice at the University of Rhode Island, specializing in the areas of epidemiology and causal inference. She has collaborated on HIV/AIDS research working closely with colleagues both domestically and internationally to develop and apply causal methodology to improve treatment and prevention of HIV/AIDS. More recently, her substantive research area has expanded to studying opioid use disorder employing a variety of “big data” sources, including administrative claims data. Her current methodological research supported by the Avenir Award from the National Institute on Drug Abuse focuses on the development and application of causal inference methods for network-based studies of HIV treatment and prevention among people who use drugs.
Laura Forastiere, PhD, is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. Her methodological research is focused on methods for assessing causal inference for evidence-based research, exploring the mechanisms underlying the effect of an intervention including causal pathways through intermediate variables or mechanisms of peer influence and spillover between connected units. Her research explores modeling, inferential, and other methodological issues that often arise in applied problems with complex clustered and network data, and standard statistical theory and methods are no longer adequate to support the goals of the analysis. Laura is eager to apply advanced statistical methodology to provide evidence on effective strategies to improve the health and wellbeing of vulnerable populations. She is particularly interested in exploring behavioral interventions that, relying on theories of behavioral economics and social phycology, exploit social interactions and peer influence among individuals. She is involved in many program evaluations and research studies in low- and middle-income countries on malaria, HIV and other STDs, maternal and child health, nutrition, cognitive development, health insurance and microcredit. Dr. Forastiere received her Ph.D. in statistics from the University of Florence (Italy) and postdoc training in statistics and biostatistics at Harvard University. Prior to joining the Department of Biostatistics at Yale School of Public Health, she was a Postdoctoral Associate in the Yale Institute for Network Science.
Gabrielle Lemire, MSc, is a Research Associate with Dr. Buchanan on the URI Avenir Team.
Betsy Ogburn, PhD, is Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. She is also senior fellow of the Good Science Project, affiliated faculty of the Center for Causal Inference at University of Pennsylvania, and member of the Institute for Data-Intensive Engineering and Science at Johns Hopkins University. She works on causal inference, including interference and social networks, measurement error, semiparametric estimation, instrumental variables methods, and mediation analysis. Currently, her main areas of research are unmeasured confounding and settings with statistical dependence. Betsy completed her Ph.D. in Biostatistics at Harvard University. She is a 2016 National Academy of Science Kavli Fellow and 2022 winner of the COPSS Emerging Leader Award.
Ke Zhang, MSc (anticipated 2023), is a Graduate Research Assistant with Dr. Buchanan on the URI Avenir team.