MacMillan-CSAP Workshop on Quantitative Research Methods: Sherri Rose, “Machine Learning for Effect Estimation in International Health”
“Machine Learning for Effect Estimation in International Health”
Guest Speaker: Sherri Rose, Assistant Professor of Biostatistics, Department of Health Care Policy, Harvard Medical School
Abstract: The impact of chronic disease on prosperity outcomes, such as poverty, has not yet been determined in many resource-limited settings. Due to a lack of health-systems focus on chronic disease, there is a preventable load of premature mortality from chronic disease. Our new statistical work was driven by data available in a health and demographic surveillance system in Bangladesh, linked with a novel economic impact survey collected via a cohort matched design. Matching based on exposure in cohort studies is not implemented as frequently as other types of matching, such as in case-control designs. A new method for robust statistical machine learning in matched cohort studies will be presented. The findings from our study will contribute to isolating the extent of the causal effect between chronic disease and poverty in Bangladesh, quantifying the potential maximum benefit of interventions, such as improved health services, to reduce death from chronic disease.
Speaker Bio: Sherri Rose, PhD is an Assistant Professor of Biostatistics in the Department of Health Care Policy at Harvard Medical School. Her work is centered around developing and integrating innovative statistical approaches to advance public health and health care. Broadly, Dr. Rose’s methodological research focuses on semiparametric estimation in causal inference and machine learning for prediction, with applications in risk adjustment and health care program impact evaluation, among others. She recently co-authored the book “Targeted Learning: Causal Inference for Observational and Experimental Data” for the Springer Series in Statistics. Dr. Rose received her Ph.D. in biostatistics from the University of California, Berkeley where her doctoral work was honored with the Evelyn Fix Memorial Prize.