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:

Publications

Accepted papers

  1. Shanshan Luo, Jiaqi Min, Wei Li, Xueli Wang*, and Zhi Geng. A comparative analysis of different adjustment sets using propensity score based estimators. To appear in Computational Statistics & Data Analysis, 2024.

  2. Shaojie Wei, Chao Zhang, Zhi Geng, and Shanshan Luo*. Identifiability and estimation for potential-outcome means with misclassified outcomes. To appear in Mathematics, 2024.

  3. Shanshan Luo, Wei Li*, Wang Miao, and Yangbo He*. Identification and estimation of causal effects in the presence of confounded principal strata. To appear in Statistics in Medicine, 2024.

  4. 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.

  5. 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.

  6. 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)

  7. 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.

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

  9. 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.

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

Working papers

  1. Wei Li, Yuan Liu, Shanshan Luo*, and Zhi Geng. Causal inference with outcomes truncated by death and missing not at random. arXiv, 2024.

  2. Shanshan Luo, Yixuan Yu, Chunchen Liu, Feng Xie*, and Zhi Geng. Assessing the causes of continuous effects by posterior effects of causes. arXiv, 2024.

  3. Shanshan Luo, Mengchen Shi, Wei Li*, Xueli Wang, and Zhi Geng. Efficiency-improved doubly robust estimation with non-confounding predictive covariates. arXiv, 2024.

  4. Peng Wu, Shanshan Luo*, and Zhi Geng. On the comparative analysis of average treatment effects estimation via data combination. arXiv, 2023.

  5. Shanshan Luo#, Yechi Zhang#, and Wei Li*. Multiply robust estimation of causal effects using linked data. arXiv, 2023.

  * Corresponding author,   # Co-first author

Teaching