معرفی
Razieh Nabi is the Rollins Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University’s Rollins School of Public Health. Her research focuses on developing novel causal methodologies to address challenges such as unmeasured confounders, missing data, and biases in healthcare and social justice contexts. She integrates machine learning, graphical models, and semiparametric statistics to create robust frameworks for causal inference.
- Education: PhD (Johns Hopkins University), M.Sc. (University of Texas at El Paso), B.Sc. (Sharif University of Technology)
- Affiliations: Active on Twitter (@raziehnabi), affiliated with Emory University
Her research interests include causal inference under complex data constraints, fair machine learning, and applications in healthcare equity. Notable work addresses racial disparities in healthcare, missing data mechanisms, and algorithmic fairness. She has developed methods such as the Ananke Python package for causal discovery and inference using graphical models.
Her articles explore topics like path-specific causal effects, semiparametric sensitivity analysis, and fair risk minimization. These contributions bridge theoretical advancements with practical applications in public health and policy.
No scientific awards are explicitly listed. She teaches courses on causal inference, directed study, and advanced biostatistics, reflecting her commitment to both research and education.
