Shanshan Luo   Department of Applied Statistics & Causal Inference Group 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 currently a lecturer in the Department of Applied 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).

Research Interests

• Causal inference: attribution analysis, causal discovery, covariate adjustment, data fusion, instrumental variable, principal stratification, propensity score, spillover effect

• Missing data: nonignorable missing data

Publications

Accepted papers

• Haoxuan Li, Shuyi Wang, Honglei Zhang, 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. Accepted by ICDE, 2024.

• Wei Li, Shanshan Luo, and Wangli Xu. Calibrated regression estimation using empirical likelihood under data fusion. Accepted by Computational Statistics & Data Analysis, 2023.

• 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.

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

Unpublished manuscripts

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

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

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

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

• Kang Shuai, Shanshan Luo, Wei Li, and Yangbo He. Identifying causal effects using instrumental variables from the auxiliary population. arXiv, 2023.

• Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He. Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates. arXiv, 2023.

• Shanshan Luo, Wei Li, Wang Miao, and Yangbo He. Identification and estimation of causal effects in the presence of confounded principal strata. arXiv, 2022.

 * Corresponding author

Teaching

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