Peng Wu   Department of Applied Statistics, Beijing Technology and Business University Beijing, China. 102488. Office: Room 209, Shutong Building, Higher Education Garden, Liangxiang, Email: pengwu@btbu.edu.cn Google Scholar

Biography

I am an Associate Professor in the Department of Applied Statistics at Beijing Technology and Business University (BTBU), and a member of the Causal Inference Group at BTBU directed by Prof. Zhi Geng. Before joining BTBU, I did postdoctoral research in the Beijing International Center for Mathematical Research at Peking University from 2020 to 2022, working with Prof. Xiaohua Zhou. I obtained my PhD in the School of Statistics at Beijing Normal University (2015 - 2020), supervised by Prof. Xingwei Tong.

Research Interests:

• Causal Inference: Heterogeneous/Individual Treatment Effect, Long-Term and Short-Term Treatment Effect, Data Fusion, Principal Stratification, Matching, Interference, Attribution

• Missing Data

• Trustworthy AI, Policy Evaluation and Learning, Recommender System

• Medical Decision-Making

Publications

*Corresponding author, #contributed equally

Wroking Papers

• Peng Wu and Linbo Wang. Position: A Potential Outcomes Perspective on Pearl's Causal Hierarchy. arXiv:2601.20405 (2026+)

• Shanshan Luo, Peng Wu*, and Zhi Geng. Pseudo-strata learning via maximizing misclassification reward. arXiv:2505.05308 (2026+)

• Peng Wu, Qing Jiang, Shanshan Luo, and Zhi Geng. Safe Individualized Treatment Rules with Controllable Harm Rates. arXiv:2505.05308 (2026+)

• Peng Wu and Xiaojie Mao. The Promises of Multiple Experiments: Identifying Joint Distribution of Potential Outcomes. arXiv:2504.20470 (2026+)

• Peng Wu, Peng Ding, Zhi Geng, and Yue Liu. Quantifying Individual Risk for Binary Outcome. arXiv:2402.10537 (2026+)

Journal

• 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

• Wenjie Hu, Xiao-Hua Zhou, and Peng Wu*. Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data. Statistica Sinica, 2025, 35:959-980

• Peng Wu, Zhiqiang Tan, Wenjie Hu, and Xiao-Hua Zhou. Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data. Statistica Sinica, 2024, 34:459-479

• Peng Wu#, Shasha Han#, Xingwei Tong, and Runze Li. Propensity score regression for causal inference with treatment heterogeneity. Statistica Sinica, 2024, 34:747-769

• Ye Tian, Peng Wu, and Zhiqiang Tan. Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data. Statistica Sinica, 2025

• Peng Wu, Pengtao Zeng, Zhaoqing Tian, and Shoajie Wei. Matching-Based Nonparametric Estimation of Group Average Treatment Effects . Statistics in Medicine, 2026

• Zhaoqing Tian and Peng Wu*. Semiparametric Efficient Inference for the Probability of Necessary and Sufficient Causation. Statistics in Medicine, 2025, 44(18-19):e70242

• Peng Wu, Xinyi Xu, Xingwei Tong, Qing Jiang, and Bo Lu. Semi-parametric Estimation for Average Causal Effects using Propensity Score based Spline. Journal of statistical planning and inference, 2021, 212:153-168

• Peng Wu, Baosheng Liang, Yifan Xia, and Xingwei Tong. Predicting Disease Risk by Matching Quantile estimation for Censored Data. Mathematical Biosciences and Engineering, 2020, 17(5):4544-4562

• Na Xu, Peng Wu, Gang Ma, Qirui Hu, Xiuqing Hu, Ronghua Wu, Yunfeng Wang, Hanlie Xu, Lin Chen, and Peng Zhang. In-flight spectral response function retrieval of a multi-spectral radiometer based on the functional data analysis technique. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-10

• Zhihui Yang#, Shasha Han#, Peng Wu#, Mingyue Wang, Ruoyu Li, Xiaohua Zhou, and Hang Li. Modeling post-treatment prognosis of skin lesions in psoriasis: A large cohort study in China. JAMA Network Open, 2023, 6(4):e236795

Conference

• Qinwei Yang, Jingyi Li, and Peng Wu*. Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls. NeurIPS 25

• Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, and Kun Zhang. Learning Counterfactual Outcomes Under Rank Preservation. NeurIPS 25

• Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu*. Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards. NeurIPS 24

• Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Fuli Feng, Xiangnan He, Zhi Geng, and Peng Wu*. Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. NeurIPS 23

• Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, and Xiangnan He. Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach. NeurIPS 23

• Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang , Chunchen Liu, and Yan Zeng. Policy Learning for Balancing Short-Term and Long-Term Rewards. ICML 24

• Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, and Zhi Geng. Local Causal Structure Learning in the Presence of Latent Variables. ICML 24

• Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Wang, Peng Wu, Zhi Geng, Xu Chen, and Xiao-Hua Zhou. Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Recommendation. ICML 24

• Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu*, and Peng Cui. Propensity Matters: Measuring and Enhancing Balancing for Recommendation. ICML 23

• Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu*, and Peng Wu*. Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. ICML 23

• Jiayi Guo, Haoxuan Li, Tian Ye, and Peng Wu*. A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators. ICLR 26

• Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu*, Zhi Geng, Fuli Feng, and Xiangnan He. Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation. ICLR 24

• Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Xu Chen, Zhi Geng, and Peng Cui. Adaptive Causal Balancing for Collaborative Filtering. ICLR 24

• Haoxuan Li, Yan Lyu, Chunyuan Zheng, and Peng Wu*. TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. ICLR 23

• Haoxuan Li, Chunyuan Zheng, and Peng Wu*. StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. ICLR 23

• Weiqin Yang, Jiawei Chen, Shengjia Zhang, Peng Wu, Yuegang Sun, Yan Feng, Chun Chen, and Can Wang. Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systems. KDD 25

• Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, and Peng Cui. Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. KDD 23

• Sihao Ding, Peng Wu*, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, and Yongdong Zhang. Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. KDD 22

• Quanyu Dai, Haoxuan Li, Peng Wu*, Zhenhua Dong, Xiao-Hua Zhou*, Rui Zhang, Xiuqiang He, Rui Zhang, and Jie Sun. A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. KDD 22

• Haoxuan Li, Quanyu Dai, Zhenhua Dong, Xiao-Hua Zhou, and Peng Wu*. Multiple Robust Learning for Recommendation. AAAI 23

• Xueqing Liu, Qinwei Yang, Zhaoqing Tian, Ruocheng Guo, and Peng Wu*. Optimal Policy Adaptation under Covariate Shift. IJCAI 25

• Peng Wu#, Haoxuan Li#, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, and Xiao-Hua Zhou. On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. IJCAI 22

• Shengjia Zhang, Weiqin Yang, Jiawei Chen, Peng Wu, Yuegang Sun, Gang Wang, Qihao Shi, and Can Wang. Talos:Optimizing Top-K Accuracy in Recommender Systems. WWW 26

• Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, and Peng Wu*. Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. WWW 23

• Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, and Qingsong Hua. Treatment Effect Estimation for User Interest Exploration on Recommender Systems. SIGIR 24

• Haoxuan Li, Shuyi Wang, Honglei Zhang, Chunyuan Zheng, Xu Chen, Li Liu, Shanshan Luo*, and Peng Wu*. Uncovering the Propensity Identification Problem in Debiased Recommendations. ICDE 24

• Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, and Xiangnan He . Causal Recommendation: Progresses and Future Directions. Tutorial on SIGIR 23

• Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, and Xiangnan He. Causal Recommendation: Progresses and Future Directions. Tutorial on WWW 22

Teaching

Applied Stochastic Processes, Fall 2022.

Data Mining, Spring 2023, 2024, 2025.

Data Analysis and Statistical Software (R), Fall 2023.

Data Analysis with Python, Fall 2024, Spring 2025, Fall 2025.

Academic Service

Area Chair: NeurIPS (2024), ICML (2025, 2026), ICLR (2026), NeurIPS Datasets and Benchmarks Track (2025).

Reviewer: Journal of the Royal Statistical Society Series B, Journal of the American Statistical Association, Journal of Machine Learning Research, Statistica Sinica, Statistics in Medicine, Journal of Causal Inference, Canadian Journal of Statistics.