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Data Science Seminar Date: Thursday, 11 September 2025 Time: 10:30 - 11:30 AM Venue: NKN Hall Integrating data for causal inference Elizabeth Stuart Department of Biostatistics, Johns Hopkins University, USA. 11-09-25 Abstract Many causal questions of interest cannot be answered through analysis of a single dataset, and as data becomes increasingly available, there is more and more interest in leveraging that data to answer nuanced questions. Such questions might include examining the generalizability of randomized trial results to target populations, to better understanding of effect heterogeneity by combining small (unbiased) randomized trials with large (but confounded) non-experimental data sources. This talk will discuss methods for causal inference in such integrated datasets, including both the promise and potential for doing so, as well as implementation challenges, such as when the measures in the different data sources are discordant. Motivating examples will come from medicine and public health, and with lessons for a range of fields, and with final comments on the broader field of evidence synthesis for causal inference. Short-Bio: Elizabeth A. Stuart is the Frank Hurley & Catharine Dorrier Professor and Chair of the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She is also a Bloomberg Professor of American Health with joint appointments in Mental Health and in Health Policy & Management. Her work focuses on causal inference, especially propensity score methods, generalisability/transportability of trial results, and policy evaluation in areas like mental health, substance use, and gun-violence prevention. Selected honours include Fellow of the American Statistical Association (2014) and Fellow of AAAS (2020), along with awards such as the Gertrude Cox Award and Harvard’s Myrto Lefkopoulou Award.
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