Associate Professor, Department of Population Health
My research focuses on the development of non-parametric statistical methods for causal inference from observational and randomized studies with complex datasets, using machine learning. This includes but is not limited to mediation analysis, methods for continuous exposures, longitudinal data including survival analysis, and efficiency guarantees with covariate adjustment in randomized trials. I am also interested in general semi-parametric theory, machine learning, and high-dimensional data.
My substantive research has so far focused on clinical applications, specifically neurology, substance use disorder, pulmonary and critical care, and precision medicine for cancer.
More information here: https://idiaz.xyz/
Associate Professor, Department of Population Health at NYU Grossman School of Medicine
PhD from University of California, Berkeley
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Journal of the American Statistical Association. 2021;
Biometrika. 2021; 108(3):627-641
Journal of the Royal Statistical Society. Series B, Statistical methodology. 2020; 82(3):661-683
Statistics in medicine. 2019 07 10; 38(15):2735-2748
Journal of statistical planning & inference. 2017; 190:39-51
Biometrics. 2016 06; 72(2):422-31
JAMA internal medicine. 2025 Nov 01; 185(11):1341-1348