Shanshan Luo   Department of Applied Statistics & Causal Inference Group School of Mathematics and Statistics Beijing Technology and Business University Beijing, China. 102488. Office: Room 209, Shutong Building, Higher Education Garden, Liangxiang, Email: shanshanluo@btbu.edu.cn

Biography

I am a lecturer in the Department of Applied Statistics, School of Mathematics and Statistics, at Beijing Technology and Business University (September 2022 - Present). Before this, I obtained my Ph.D. degree in the School of Mathematical Sciences at Peking University (September 2017 - July 2022), supervised by Prof Yangbo He. I obtained my B.S. degree in the School of Mathematical Sciences at Capital Normal University (September 2013 - July 2017). Here is my CV.

Research Interests:

My main focus is on causal inference, with primary interest in the following four topics:

• Causal effect: covariate adjustment, data fusion, instrumental variable, measurement error, principal stratification, propensity score, spillover effect
• Causal attribution: individual attribution analysis, continuous outcome attribution
• Causal discovery: Bayesian network, causal mechanism of latent confounders, proximal variable selection
• Missing data: nonignorable missing data

Publications

1. Peng Wu, Shanshan Luo, and Zhi Geng. On the comparative analysis of average treatment effects estimation via data combination. To appear in Journal of the American Statistical Association, 2024.

2. Shanshan Luo, Jiaqi Min, Wei Li, Xueli Wang, and Zhi Geng. A comparative analysis of different adjustment sets using propensity score based estimators. Computational Statistics & Data Analysis, 2025; 203: 108079.

3. Shaojie Wei, Chao Zhang, Zhi Geng, and Shanshan Luo. Identifiability and estimation for potential-outcome means with misclassified outcomes. Mathematics, 2024; 12(18):2801.

4. Shanshan Luo, Wei Li, Wang Miao, and Yangbo He. Identification and estimation of causal effects in the presence of confounded principal strata. Statistics in Medicine, 2024; 43(22): 4372-4387.

5. Kang Shuai, Shanshan Luo, Wei Li, and Yangbo He. Identifying causal effects using instrumental variables from the auxiliary population. To appear in Statistica Sinica, 2024.

6. Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He. Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates. To appear in Statistica Sinica, 2024.

7. Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, and Zhi Geng. Automating the selection of proxy variables of unmeasured confounders. ICML, Vienna, Austria, 2024. (Spotlight)

8. Honglei Zhang, Shuyi Wang, Haoxuan Li, Chunyuan Zheng, Xu Chen, Li Liu, Shanshan Luo, and Peng Wu. Uncovering the limitations of eliminating selection bias for recommendation: missing mechanisms, disentanglement, and identifiability. ICDE, Utrecht, Netherlands, 2024.

9. Wei Li, Shanshan Luo, and Wangli Xu. Calibrated regression estimation using empirical likelihood under data fusion. Computational Statistics & Data Analysis, 2024; 190: 107871.

10. Wei Li, Shanshan Luo, Yangbo He, and Zhi Geng. Subgroup analysis using Bernoulli-gated hierarchical mixtures of experts models. Statistics in Medicine, 2023; 42(26): 4681–4695.

11. Shanshan Luo, Wei Li, and Yangbo He. Causal inference with outcomes truncated by death in multiarm studies. Biometrics, 2023; 79(1): 502-513.

Teaching

• Applied Stochastic Processes: Fall 2022.
• Multivariate Statistical Analysis: Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025.
• Causal Inference: Spring 2023, Fall 2023, Fall 2024, Spring 2025.