Weilin Chen

Boya Postdoctoral Researcher

Peking University
Beijing, China

Email: chenweilin.chn@gmail.com
Mobile: +86-131-4578-0665
Links: Google Scholar | GitHub
Portrait of Weilin Chen

Biography

Weilin Chen is a Boya postdoctoral researcher at Peking University, working with Professor Zhouchen Lin. Previously, from June 2025 to May 2026, he was a postdoctoral researcher at Guangdong University of Technology, working with Professor Ruichu Cai. He received his Ph.D. in Computer Science from Guangdong University of Technology in 2025, supervised by Professor Ruichu Cai, and his B.S. degree in Software Engineering from the same university in 2020. From June to December 2024, he was a visiting student at the University of Cambridge, supervised by Professor Jose Miguel Hernandez-Lobato.

His research focuses on causal inference, causal discovery, and related applications, especially long-term causal effect estimation and networked effect estimation. His work has been published in venues including ICML, WWW, IJCAI, AAAI, ICLR, CVPR, Neural Networks, and TNNLS.

Education

Experience

Research Interests

Publications

  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

Awards

Services