Ruyi Liu

Project Title: Toward a New Generation of Causal Hazard Ratio: A Bayesian Machine Learning Approach for Survival Analysis

Ruyi Liu is a third-year Ph.D. student in the Department of Biostatistics at the Yale School of Public Health. Her research leverages advanced Bayesian tools, including Bayesian nonparametric methods, to address complex methodological challenges in causal inference, with a focus on applications in cluster-randomized trials and observational studies. As a CERSI Scholar, her project focuses on developing a novel Bayesian nonparametric framework for identifying and estimating causally interpretable hazard ratio estimands with coherent propagation of uncertainty for applications in survival analysis.