José R. Zubizarreta

José R. Zubizarreta is a Professor in the Department of Health Care Policy at Harvard Medical School, a Professor in the Department of Biostatistics at Harvard School of Public Health, and a Faculty Affiliate in the Department of Statistics at Harvard University's Faculty of Arts and Sciences. His research focuses on developing statistical methods for causal inference, particularly in healthcare and public policy contexts. He has received multiple awards, including the Kenneth Rothman Award and the Tom Ten Have Memorial Award, and is a Fellow of the American Statistical Association.

He holds editorial roles at prominent journals such as the Annals of Applied Statistics and the Journal of the American Statistical Association. His work includes co-editing the Handbook of Matching and Weighting Adjustments for Causal Inference, a seminal reference in the field. Zubizarreta’s research addresses challenges in observational studies, including methods for bias reduction, generalization of results, and applications in healthcare quality measurement and policy evaluation.

Notable contributions include advancements in optimal matching techniques, balancing weights, and scalable methods for analyzing large observational datasets. His collaborative projects span interdisciplinary areas such as healthcare economics, epidemiology, and machine learning applications in causal inference. Zubizarreta actively contributes to software development, including R packages like mipmatch and designmatch, which are widely used in statistical research.

His academic service includes roles in editorial boards and grant review panels, reflecting his leadership in advancing statistical methodology and its applications. Zubizarreta’s work bridges theoretical rigor with practical impact, addressing pressing questions in healthcare systems and public policy.