邮箱：shanshanluo@btbu.edu.cn

地址：北京市房山区永利官网良乡主校区东区3044AM永利集团官网

个人简介

现为讲师、硕士生导师，理学博士，中国现场统计研究会因果推断分会副秘书长。已指导多名硕士研究生顺利毕业，部分员工毕业后进入高校、政府部门或事业单位，从事数据分析、统计建模等相关工作，同时也参与协助多名博士研究生的培养工作。研究方向主要包括因果推断及其在生物医学、人工智能和社会科学等领域的应用。

研究兴趣

因果推断方法的理论与应用，涵盖归因分析、主分层分析、工具变量方法、溢出效应以及基于回归的协变量调整；同时关注其在生物医学、人工智能与社会科学等领域中的实际应用问题。更多信息可参考个人主页：http://shanshanluo.cn

主讲课程

本科生课程《多元统计分析》，2022年 - 至今

研究生课程《统计因果推断》，2022年 - 至今

研究生课程《应用随机过程》，2022年秋季

学习经历

2013年 9 月 - 2017年7月，首都师范大学数学科学学院，理学学士；

2017年 9 月 - 2022年7月，北京大学数学科学学院，理学博士。

工作经历

2022年9月 - 至今，3044AM永利集团官网，讲师。

主要获奖荣誉

1.全国老员工市场调查与分析大赛，国家三等奖，指导教师，2025
2.京津冀高校立丰杯市场调查与分析大赛，校级三等奖，指导教师，2025

主要科研项目

1.国家自然科学基金青年项目，2025.01-2027.12，主持

主要学术成果

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

2.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.

3. 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%)

4. Shanshan Luo, Yechi Zhang, Wei Li, and Zhi Geng. Multiply robust estimation of causal effects using linked data. To appear in Computational Statistics & Data Analysis, 2025.

5. 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.

6. 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.

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

8. 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.

9. 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.

10. 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.

11. 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%)

12. 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.

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

14. 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.

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

* 通讯作者