| 2026 | AAAI | Uplift Modeling with Delayed Feedback: Identifiability and Algorithms. | Chunyuan Zheng, Anpeng Wu, Chuan Zhou, Taojun Hu, Qingying Chen, Hongyi Liu, Chenxi Li, Huiyou Jiang, Haoxuan Li, Zhouchen Lin |
| 2026 | KDD | Continuous-Time Counterfactual Quantile Learning for Risk-Sensitive Policy Optimization. | Yi He, Anpeng Wu, Ruoxuan Xiong, Yingrong Wang, Kun Kuang |
| 2025 | ICLR | Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference. | Anpeng Wu, Haiyi Qiu, Zhengming Chen, Zijian Li, Ruoxuan Xiong, Fei Wu, Kun Zhang |
| 2025 | ICML | Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments. | Baohong Li, Yingrong Wang, Anpeng Wu, Ming Ma, Ruoxuan Xiong, Kun Kuang |
| 2025 | ICML | Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency. | Zexu Sun, Qiyu Han, Hao Yang, Anpeng Wu, Minqin Zhu, Dugang Liu, Chen Ma, Yunpeng Weng, Xing Tang, Xiuqiang He |
| 2025 | ICML | Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation. | Minqin Zhu, Zexu Sun, Ruoxuan Xiong, Anpeng Wu, Baohong Li, Caizhi Tang, Jun Zhou, Fei Wu, Kun Kuang |
| 2025 | KDD | Classifying Treatment Responders: Bounds and Algorithms. | Anpeng Wu, Haoxuan Li, Chunyuan Zheng, Kun Kuang, Kun Zhang |
| 2024 | AAAI | Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation. | Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang |
| 2024 | ICDE | Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations. | Yuling Zhang, Anpeng Wu, Kun Kuang, Liang Du, Zixun Sun, Zhi Wang |
| 2024 | ICML | A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective. | Baohong Li, Haoxuan Li, Anpeng Wu, Minqin Zhu, Shiyuan Peng, Qingyu Cao, Kun Kuang |
| 2024 | ICML | Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias. | Baohong Li, Haoxuan Li, Ruoxuan Xiong, Anpeng Wu, Fei Wu, Kun Kuang |
| 2024 | ICML | Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias. | Baohong Li, Anpeng Wu, Ruoxuan Xiong, Kun Kuang |
| 2024 | ICML | Learning Causal Relations from Subsampled Time Series with Two Time-Slices. | Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu |
| 2023 | AAAI | Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation. | Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu |
| 2023 | ICML | Stable Estimation of Heterogeneous Treatment Effects. | Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu |
| 2022 | ICML | Instrumental Variable Regression with Confounder Balancing. | Anpeng Wu, Kun Kuang, Bo Li, Fei Wu |
| 2022 | KDD | Estimating Individualized Causal Effect with Confounded Instruments. | Haotian Wang, Wenjing Yang, Longqi Yang, Anpeng Wu, Liyang Xu, Jing Ren, Fei Wu, Kun Kuang |