| On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges P Wu, H Li, Y Deng, W Hu, Q Dai, Z Dong, J Sun, R Zhang, XH Zhou International Joint Conference on Artificial Intelligence (2022), 2022 | 53 | 2022 |
| StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random H Li, C Zheng, P Wu The Eleventh International Conference on Learning Representations, 2023 | 37* | 2023 |
| Addressing unmeasured confounder for recommendation with sensitivity analysis S Ding, P Wu, F Feng, Y Wang, X He, Y Liao, Y Zhang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 34 | 2022 |
| A generalized doubly robust learning framework for debiasing post-click conversion rate prediction Q Dai, H Li, P Wu, Z Dong, XH Zhou, R Zhang, R Zhang, J Sun Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 32 | 2022 |
| Balancing unobserved confounding with a few unbiased ratings in debiased recommendations H Li, Y Xiao, C Zheng, P Wu Proceedings of the ACM Web Conference 2023, 1305-1313, 2023 | 27 | 2023 |
| TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations H Li, Y Lyu, C Zheng, P Wu The Eleventh International Conference on Learning Representations, 2023 | 26 | 2023 |
| Multiple robust learning for recommendation H Li, Q Dai, Y Li, Y Lyu, Z Dong, XH Zhou, P Wu Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4417-4425, 2023 | 23 | 2023 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation H Li, Y Xiao, C Zheng, P Wu, P Cui Proceedings of the 40-th International Conference on Machine Learning, 2023 | 20 | 2023 |
| Trustworthy Policy Learning under the Counterfactual No-Harm Criterion H Li, C Zheng, Y Cao, Z Geng, Y Liu, P Wu Proceedings of the 40-th International Conference on Machine Learning, 2023 | 17 | 2023 |
| Causal recommendation: Progresses and future directions W Wang, Y Zhang, H Li, P Wu, F Feng, X He Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 16 | 2023 |
| Propensity score regression for causal inference with treatment heterogeneity P Wu, SS Han, X Tong, R Li Statistica Sinica, 2022 | 12 | 2022 |
| Removing hidden confounding in recommendation: a unified multi-task learning approach H Li, K Wu, C Zheng, Y Xiao, H Wang, Z Geng, F Feng, X He, P Wu Advances in Neural Information Processing Systems 36, 2024 | 10 | 2024 |
| Who should be given incentives? counterfactual optimal treatment regimes learning for recommendation H Li, C Zheng, P Wu, K Kuang, Y Liu, P Cui Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 10 | 2023 |
| Model-assisted inference for covariate-specific treatment effects with high-dimensional data P Wu, Z Tan, W Hu, XH Zhou Statistica Sinica, 2022 | 10 | 2022 |
| Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data W Hu, X Zhou, P Wu Statistica Sinica, 2023 | 8 | 2023 |
| Semiparametric estimation for average causal effects using propensity score-based spline P Wu, X Xu, X Tong, Q Jiang, B Lu Journal of Statistical Planning and Inference 212, 153-168, 2021 | 7 | 2021 |
| Regression and subgroup detection for heterogeneous samples B Liang, P Wu, X Tong, Y Qiu Computational Statistics 35, 1853-1878, 2020 | 6 | 2020 |
| Debiased collaborative filtering with kernel-based causal balancing H Li, C Zheng, Y Xiao, P Wu, Z Geng, X Chen, P Cui arXiv preprint arXiv:2404.19596, 2024 | 4 | 2024 |
| On the comparative analysis of average treatment effects estimation via data combination P Wu, S Luo, Z Geng arXiv preprint arXiv:2311.00528, 2023 | 4 | 2023 |
| 基于 Copula 函数的海南热带气旋风雨联合概率特征分析 侯静惟， 方伟华， 程锰， 叶妍婷， 吴鹏， 韩轶男 自然灾害学报 28 (3), 54-64, 2019 | 4 | 2019 |
Haoxuan LiPeking UniversityVerified email at stu.pku.edu.cn
