Bio

Shu Yang is Associate Professor of Statistics at North Carolina State University. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data and spatial statistics.

Website: https://shuyang.wordpress.ncsu.edu/

Research Impact Dashboard

Area(s) of Expertise

Causal inference in longitudinal observational data
Semiparametric efficient estimation
Missing data analysis and imputation methods

Publications

• A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes , Journal of the American Statistical Association (2026)
• Augmented match weighted estimators: new methods for estimating average treatment effects under extreme propensity scores , Journal of Biopharmaceutical Statistics (2026)
• Bayesian Structured Mediation analysis with Unobserved confounders , Biometrics (2026)
• Improving Sensitivity Analysis By Synthesizing Randomized Clinical Trials With Limited Overlap , Statistics in Medicine (2026)
• A practical analysis procedure on generalizing comparative effectiveness in the randomized clinical trial to the real-world trial-eligible population , Journal of Biopharmaceutical Statistics (2025)
• COADVISE: covariate adjustment with variable selection in randomized controlled trials , Journal of the Royal Statistical Society Series A: Statistics in Society (2025)
• Data fusion methods for the heterogeneity of treatment effect and confounding function , Bernoulli (2025)
• Discussion on "Causal and Counterfactual Views of Missing Data Models" , Statistica Sinica (2025)
• Double machine learning methods for estimating average treatment effects: a comparative study , Journal of Biopharmaceutical Statistics (2025)
• Doubly Robust Fusion of High-Dimensional Treatments for Policy Learning , 41st (ICML) International Conference on Machine Learning (2025)