陈伟霖

北京大学博雅博士后研究员

北京大学
中国，北京

邮箱：chenweilin.chn@gmail.com
电话号码：+86-131-4578-0665
链接：Google Scholar | GitHub
陈伟霖照片

个人简介

陈伟霖现为北京大学博雅博士后研究员，指导老师为林宙辰教授。此前，他于 2025 年 6 月至 2026 年 5 月在广东工业大学从事博士后研究，指导老师为蔡瑞初教授。他于 2025 年获广东工业大学计算机科学博士学位，导师为蔡瑞初教授；于 2020 年获该校软件工程学士学位。2024 年 6 月至 12 月，他在剑桥大学担任访问学生，导师为Jose Miguel Hernandez-Lobato 教授。

研究方向聚焦于因果推断、因果发现及相关应用，尤其是长期因果效应估计和网络干扰下的效应估计。相关工作发表于 ICML、WWW、IJCAI、AAAI、ICLR、CVPR、Neural Networks 和 TNNLS 等学术会议与期刊。

教育经历

工作经历

研究兴趣

论文发表

  1. Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao. Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. ICML, 2024 (Oral). Paper Code
  2. Weilin Chen, Jie Qiao, Ruichu Cai, Zhifeng Hao. On the Role of Entropy-Based Loss for Learning Causal Structure with Continuous Optimization. TNNLS, 2023. Paper Code
  3. Ruichu Cai*, Weilin Chen*, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing Yang, Jiecheng Guo. Long-term Causal Effects Estimation via Latent Surrogates Representation Learning. Neural Networks, 2024. Paper Code
  4. Weilin Chen, Ruichu Cai, Jie Qiao, Yuguang Yan, Jose Miguel Hernandez-Lobato. Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption. arXiv. Paper
  5. Weilin Chen, Ruichu Cai, Junjie Wan, Zeqin Yang, Jose Miguel Hernandez-Lobato. Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination. arXiv. Paper
  6. Weilin Chen, Ruichu Cai, Yuguang Yan, Zhifeng Hao, Jose Miguel Hernandez-Lobato. Long-term Causal Inference via Modeling Sequential Latent Confounding. arXiv. Paper
  7. Zeqin Yang*, Weilin Chen*, Ruichu Cai, Yuguang Yan, Zhifeng Hao, Zhipeng Yu, Zhichao Zou, Jixing Xu, Zhen Peng, Jiecheng Guo. Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights. arXiv. Paper
  8. Peilin Zhan*, Wei Chen*, Weilin Chen*, Shuyi Pan, Ruichu Cai. Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing. IJCAI, 2026. Paper Code
  9. Yuguang Yan, Haolin Yang, Zecong Chen, Weilin Chen, Ruichu Cai, Zhifeng Hao. Adjusting Prediction Model Through Wasserstein Geodesic for Causal Inference. ICLR, 2026. Paper
  10. Yuguang Yan, Haolin Yang, Shihao Zhang, Weilin Chen, Ruichu Cai, Zhifeng Hao. Matching without Group Barrier for Heterogeneous Treatment Effect Estimation. ICLR, 2026. Paper
  11. Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui. CDFM: Towards a General-Purpose Causal Discovery Foundation Model. arXiv. Paper Code
  12. Ruichu Cai, Junjie Wan, Weilin Chen, Zeqin Yang, Zijian Li, Peng Zhen, Jiecheng Guo. Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning. IJCAI, 2025. Paper Code
  13. Jiabi Zheng, Weilin Chen, Zhiyong Lin, Aqing Yang, Zhifeng Hao. Long-term Causal Effects Estimation across Domains: an Invariant Surrogate Representation Learning Approach. IJMLC, 2025. Paper
  14. Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai, Zhifeng Hao. Reducing Balancing Error for Causal Inference via Optimal Transport. ICML, 2024. Paper
  15. Yuguang Yan, Zeqin Yang, Weilin Chen, Ruichu Cai, Zhifeng Hao, Michael Kwok-Po Ng. Exploiting Geometry for Treatment Effect Estimation via Optimal Transport. AAAI, 2024. Paper
  16. Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao. Generalization Bound for Estimating Causal Effects from Observational Network Data. CIKM, 2023. Paper Code
  17. Junxian Huang, Ruichu Cai, Hao Zhu, Juntao Fang, Boyan Xu, Weilin Chen, Zijian Li, Shenghua Gao. Hierarchical Action Learning for Weakly-Supervised Action Segmentation. CVPR, 2026. Paper Code
  18. Ruichu Cai, Zhifan Jiang, Kaitao Zheng, Zijian Li, Weilin Chen, Xuexin Chen, Yifan Shen, Guangyi Chen, Zhifeng Hao, Kun Zhang. Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. WWW, 2025. Paper Code

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