Shanshan Luo

Associate Professor

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

Shanshan Luo

Biography

I was recently promoted to Associate Professor in the Department of Applied Statistics at the School of Mathematics and Statistics, Beijing Technology and Business University (January 2026 - present), where I previously served as a Lecturer (September 2022 - December 2025).

I obtained my Ph.D. in the School of Mathematical Sciences at Peking University (September 2017 - July 2022) under the supervision of Prof. Yangbo He, and my B.S. in the School of Mathematical Sciences at Capital Normal University (September 2013 - July 2017). I am currently a visiting scholar at the University of Cambridge (January 2026 - June 2026), working with Prof. Qingyuan Zhao.

Research Interests

My primary research focuses on causal inference, particularly causal mechanisms and the interpretability of causal relationships. Over the past two years, I have been interested in attribution problems, especially retrospective causal analysis and questions of responsibility fairness. I am interested in exploring the statistical assumptions necessary to establish the identifiability of key causal parameters, as well as methods for partial identification. Additionally, I am also interested in developing more flexible and robust estimation methods. I have worked on the following topics:

Causal Effect

covariate adjustment, data fusion, instrumental variable, measurement error, principal stratification, propensity score, spillover effect, truncation by death

Causal Attribution

Causes of effects, individual attribution analysis, continuous outcome attribution, interaction/synergistic attribution

Causal Discovery

Bayesian network, causal mechanism of latent confounders, proximal variable selection

Missing Data

nonignorable missing data

Applications

environmental protection, oil price forecasting, and cancer causation

Publications

  1. Jiaqi Min, Xueyue Zhang, and Shanshan Luo. A regression-based approach for bidirectional proximal causal inference. To appear in Journal of the Royal Statistical Society: Series A, 2026.

  2. Shanshan Luo, Mengchen Shi, Wei Li, Xueli Wang, and Zhi Geng. Efficiency-improved doubly robust estimation with non-confounding predictive covariates. Electronic Journal of Statistics, 2025; 19(2): 3723-3742.

  3. Shanshan Luo and Zhi Geng. Discussion on ''Causal and Counterfactual Views of Missing Data Models''. To appear in Statistica Sinica, 2025.

  4. Wei Li, Yuan Liu, Shanshan Luo, and Zhi Geng. Causal inference with outcomes truncated by death and missing not at random. To appear in Statistics in Medicine, 2025.

  5. Shanshan Luo, Yixuan Yu, Chunchen Liu, Feng Xie, and Zhi Geng. Causal attribution analysis for continuous outcomes. ICML, Vancouver, Canada, 2025. spotlight, top 2.6%

  6. Shanshan Luo, Yechi Zhang, Wei Li, and Zhi Geng. Multiply robust estimation of causal effects using linked data. Computational Statistics & Data Analysis, 2025; 209: 108175

  7. Peng Wu, Shanshan Luo, and Zhi Geng. On the comparative analysis of average treatment effects estimation via data combination. Journal of the American Statistical Association, 2025; 120(552), 2250–2261.

  8. 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.

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

  10. 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.

  11. 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.

  12. 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.

  13. 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, top 3.5%

  14. 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.

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

  16. 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.

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

Preprints

  1. Shanshan Luo, Wei Li, Xueli Wang, Shaojie Wei, and Zhi Geng. Assessing interactive causes of an occurred outcome due to two binary exposures. arXiv, 2026.

  2. Shanshan Luo, Kang Shuai, Yechi Zhang, Wei Li, and Yangbo He. Identification and estimation of causal peer effects using instrumental variables. arXiv, 2025. poster award, ICSA 2025

  3. Yue Zhang, Shanshan Luo, Zhi Geng, and Yangbo He. Optimal treatment rules under missing predictive covariates: a covariate-balancing doubly robust approach. arXiv, 2025.

  4. Naiwen Ying, Shanshan Luo, and Wang Miao. A generalized tetrad constraint for testing conditional independence given a latent variable. arXiv, 2025.

  5. Peng Wu, Qing Jiang, and Shanshan Luo. Safe individualized treatment rules with controllable harm rates. arXiv, 2025.

  6. Shanshan Luo, Peng Wu, and Zhi Geng. Policy learning with pseudo-type classification. arXiv, 2025.

  7. Shanshan Luo, Yingying Wang, and Yue Zhang. Improved inverse probability weighting estimation and its application to predictive covariates, 2025. Submitted.

  8. Yafang Deng, Kang Shuai, and Shanshan Luo. Bidirectional causal inference for binary outcomes in the presence of unmeasured confounding. arXiv, 2026.

Teaching

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