Machine Learning & Causal Inference to Improve Community Health in East Africa
When and Where
Speakers
Description
Many studies face the dual challenges of missing data and small sample sizes. In this talk, we highlight the use of causal inference and machine learning methods to reduce bias due to missing outcomes and to improve precision when estimating effects in studies with few independent units and high levels of dependence within those units. We illustrate with the SEARCH Study, a cluster randomized trial for HIV prevention and improved community health in rural Kenya and Uganda.
About Laura Balzer
Dr. Laura Balzer is an Assistant Professor of Biostatistics and Director of the Causality Lab at the UMass-Amherst. Her areas of expertise include causal inference and machine learning. These disciplines are central to developing, evaluating, and implementing data-driven solutions in Public Health and Medicine. Dr. Balzer is the Primary Statistician for four cluster randomized trials in East Africa. She is deeply committed to improving participant outcomes, building community health, translating advanced methods to applied audiences, and developing the next generation of scientists. Her work is supported by the National Institutes of Health (NIH), and has been recognized with the ASA’s Causality in Statistics Education Award. A recent profile on Dr. Balzer can be found here.