Shu Yang
SY
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 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)
- Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials , 41st (ICML) International Conference on Machine Learning (2025)
- Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events , Biometrics (2025)
- Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data , Journal of Machine Learning Research (2025)
- Efficient causal decision making with one-sided feedback , The proceedings of the 30th International Conference on Learning Representations (ICLR) (2025)