Dear Applied Statistics Workshop Community, Our next meeting will be on September 20 (12:00 EST). Larry Han presents "Promises and Perils of Multiply Robust Federated and Transfer Learning to Estimate Causal Effects." <When> September 20, 12:00 to 1:30 PM, EST Lunch will be available for pick-up at 11:45 (CGIS K354). <Where> In-person: CGIS K354 Zoom: https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09 <Abstract> Abstract: Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are challenging due to the need to preserve the privacy of each individual's data and the heterogeneity in their covariate distributions. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and covariate mismatch between sites by developing multiply-robust and privacy-preserving nuisance function estimation. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing approaches. Finally, we showcase the utility of our methodology for estimating COVID-19 vaccine efficacy (Moderna vs. Pfizer) across geographic regions, and variations in congenital heart surgery quality across racial/ethnic groups. Our findings have implications for the efficient allocation of scarce resources. Paper 1: https://arxiv.org/abs/2112.09313 Paper 2: https://arxiv.org/abs/2203.00768 Paper 3: In progress (will update with the Arxiv link soon) <2023 Schedule> GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Calendar: https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm… Best, Jialu -- Jialu Li Department of Government Harvard University jialu_li(a)g.harvard.edu