Yan Zeng's homepage

About Me LinkedIn

I am a lecturer in the Causal Inference Team, directed by Prof. Zhi Geng, in the Department of Applied Statistics at Beijing Technology and Business University (BTBU), China. I am also fortunate to work with Shohei Shimizu as a visiting scientist of RIKEN, Japan. Before joining BTBU, I worked as a postdoctoral researcher at Tsinghua University (2021 - 2023), supervised by Prof. Fuchun Sun, working on causal reinforcement learning. I obtained my ph.D. degree at Guangdong University of Technology (2016 - 2021), supervised by Prof. Zhifeng Hao, and Prof. Ruichu Cai, and co-supervised by Prof. Shohei Shimizu at RIKEN. From 2019-2020, I was a visiting Ph.D. student in the causal inference group of RIKEN.

Research Interests Team Website

My research focuses on causal machine learning, bridging theoretical causal principles with reliable decision-making and learning systems. Specifically, my interests fall into two main directions:

Causality
- Causal Structure Learning: Uncovering causal relationships from observational data, with an emphasis on latent variable discovery, unobserved confounding, local structure learning, causal discovery from mixed data and multi-domain environments, etc.
- Causal Effect Estimation: Identifying and estimating causal effects via data-driven selection of instrumental variables, adjustment sets, etc.
Causality-Empowered Machine Learning
- Causal Decision Making: Intergrating counterfactual analysis, causal interventions and other causal knowledge in causal reinforcement learning, causal imitation learning, short-term and long-term policy learning.
- Reliable Learning Systems: Enhancing model interpretability, adaptability and robustness in causal continual Learning and causal-driven representation learning, etc.

News

[2026.07] Delivered a talk titled "Confounded causal imitation learning with instrumental variables" at the 2026 Annual Academic Conference of the Causal Inference Branch of CAAS in Changchun, Jilin. [Photos]

Publications Google Scholar

^# Corresponding author, * Equal contribution.

2026

Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions

Zeyu Liu, Zheng Li, Feng Xie^#, Yan Zeng^#, Hao Zhang, Kun Zhang

International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.

Spotlight, TOP 2.6% PDF Poster

    @inproceedings{liu2026local,
        title={Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions},
        author={Liu, Zeyu and Li, Zheng and Xie, Feng and Zeng, Yan and Zhang, Hao and Zhang, Kun},
        booktitle={International Conference on Machine Learning},
        year={2026}
    }

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models

Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie^#

Journal of Machine Learning Research (JMLR), 2026.

arXiv

        @article{guo2026testability,
          title={Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models},
          author={Guo, Xichen and Li, Zheng and Huang, Biwei and Zeng, Yan and Geng, Zhi and Xie, Feng},
          journal={Journal of Machine Learning Research},
          year={2026}
        }

PrePrompt: Predictive Prompting for Class Incremental Learning

Libo Huang, Xiangqi Li, Jiarui Zhao, Zhulin An^#, Chuanguang Yang, Boyu Diao, Fei Wang, Yan Zeng, Zhifeng Hao, Yongjun Xu

SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026. [CCF-A]

Project PDF arXiv Code

        @inproceedings{huang2026preprompt,
        title={PrePrompt: Predictive prompting for class incremental learning},
        author={Libo Huang, Xiangqi Li, Jiarui Zhao, Zhulin An, Chuanguang Yang, Boyu Diao, Fei Wang,  Yan Zeng, Zhifeng Hao, Yongjun Xu},
        booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
        year={2026}
        }

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Kun Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao

Science China Information Sciences, 2026, 69(2): 122104.

PDF

        @article{cai2026learning,
          title={Learning by doing: an online causal reinforcement learning framework with causal-aware policy},
          author={Cai, Ruichu and Huang, Siyang and Qiao, Jie and Chen, Wei and Zeng, Yan and Zhang, Kun and Sun, Fuchun and Yu, Yang and Hao, Zhifeng},
          journal={Science China Information Sciences},
          volume={69},
          number={2},
          pages={122104},
          year={2026}
        }

2025

A Survey on Causal Reinforcement Learning

Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2025.

Project PDF

        @article{zeng2025survey,
            title={A Survey on Causal Reinforcement Learning},
            author={Zeng, Yan and Cai, Ruichu and Sun, Fuchun and Huang, Libo and Hao, Zhifeng},
            journal={IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)},
            year={2025},
            volume={36},
            number={4},
            pages={5942-5962},
            publisher={IEEE}
        }

Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables

Zheng Li, Xichen Guo, Feng Xie^#, Yan Zeng, Hao Zhang^#, Zhi Geng

Neural Information Processing Systems (NeurIPS), San Diego, USA, 2025.

PDF

        @inproceedings{li2025local,
            title={Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables},
            author={Li, Zheng and Guo, Xichen and Xie, Feng and Zeng, Yan and Zhang, Hao and Geng, Zhi},
            booktitle={Advances in Neural Information Processing Systems},
            year={2025}
        }

Learning Counterfactual Outcomes Under Rank Preservation

Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, Kun Zhang

Neural Information Processing Systems (NeurIPS), San Diego, USA, 2025.

PDF

    @inproceedings{wu2025learning,
    title={Learning Counterfactual Outcomes Under Rank Preservation},
    author={Wu, Peng and Li, Haoxuan and Zheng, Chunyuan and Zeng, Yan and Chen, Jiawei and Liu, Yang and Guo, Ruocheng and Zhang, Kun},
    booktitle={Advances in Neural Information Processing Systems},
    year={2025}
    }

Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models

Xichen Guo, Feng Xie^#, Yan Zeng, Hao Zhang, Zhi Geng

International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.

PDF

        @inproceedings{guo2025datadriven,
            title={Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models},
            author={Guo, Xichen and Xie, Feng and Zeng, Yan and Zhang, Hao and Geng, Zhi},
            booktitle={International Conference on Machine Learning},
            year={2025}
        }

2024

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments

Feng Xie, Zhen Yao, Lin Xie, Yan Zeng^#, Zhi Geng

Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.

Oral, TOP 0.39% PDF

        @inproceedings{xie2024identification,
          title={Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments},
          author={Xie, Feng and Yao, Zhen and Xie, Lin and Zeng, Yan and Geng, Zhi},
          booktitle={Advances in Neural Information Processing Systems},
          year={2024}
        }

Local Causal Structure Learning in the Presence of Latent Variables

Feng Xie, Zheng Li, Peng Wu, Yan Zeng^#, Chunchen Liu, Zhi Geng

International Conference on Machine Learning (ICML), 2024.

PDF Code

    @inproceedings{xie2024local,
      title={Local Causal Structure Learning in the Presence of Latent Variables},
      author={Xie, Feng and Li, Zheng and Wu, Peng and Zeng, Yan and Liu, Chunchen and Geng, Zhi},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }

Policy Learning for Balancing Short-Term and Long-Term Rewards

Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng^#

International Conference on Machine Learning (ICML), 2024.

PDF Code

    @inproceedings{wu2024policy,
      title={Policy Learning for Balancing Short-Term and Long-Term Rewards},
      author={Wu, Peng and Shen, Ziyu and Xie, Feng and Wang, Zhongyao and Liu, Chunchen and Zeng, Yan},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }

ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization

Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu

International Conference on Machine Learning (ICML), 2024.

Oral, TOP 1.5% Project PDF Code

    @inproceedings{ji2024ace,
      title={ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization},
      author={Ji, Tianying and Liang, Yongyuan and Zeng, Yan and Luo, Yu and Xu, Guowei and Guo, Jiawei and Zheng, Ruijie and Huang, Furong and Sun, Fuchun and Xu, Huazhe},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }

Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards

Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu^#

Neural Information Processing Systems (NeurIPS), 2024.

PDF

        @inproceedings{yang2024learning,
          title={Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards},
          author={Yang, Qinwei and Liu, Xueqing and Zeng, Yan and Guo, Ruocheng and Liu, Yang and Wu, Peng},
          booktitle={Advances in Neural Information Processing Systems},
          year={2024}
        }

Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity

Takashi Nicholas Maeda, Yan Zeng, Shohei Shimizu

Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis. Springer, 2024: 181-205.

PDF

        @incollection{maeda2024causal,
            title={Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity},
            author={Maeda, Takashi Nicholas and Zeng, Yan and Shimizu, Shohei},
            booktitle={Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis},
            pages={181--205},
            year={2024},
            publisher={Springer}
        }

eTag: Class-Incremental Learning via Hierarchical Embedding Distillation and Task-Oriented Generation

Libo Huang, Yan Zeng, Chuanguang Yang, Zhulin An^#, Boyu Diao, Yongjun Xu

AAAI Conference on Artificial Intelligence (AAAI), 2024.

PDF Slides Code

    @inproceedings{huang2024etag,
        title={eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation},
        author={Huang, Libo and Zeng, Yan and Yang, Chuanguang and An, Zhulin and Diao, Boyu and Xu, Yongjun},
        booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
        volume={38},
        number={11},
        pages={12591--12599},
        year={2024}
    }

KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning

Libo Huang, Zhulin An^#, Yan Zeng, Xiang Zhi, Yongjun Xu

International Conference on Learning Representations (ICLR) Tiny Papers Track, 2024

Oral PDF Code

    @inproceedings{huang2024kfc,
        title={KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning},
        author={Huang, Libo and An, Zhulin and Zeng, Yan and Xu, Yongjun and others},
        booktitle={International Conference on Learning Representations (ICLR) Tiny Papers Track},
        year={2024}
    }

2023

Python package for causal discovery based on LiNGAM

Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu

Journal of Machine Learning Research (JMLR), 2023, 24(14): 1-8.

PDF Code

        @article{ikeuchi2023python,
          title={Python package for causal discovery based on LiNGAM},
          author={Ikeuchi, Takashi and Ide, Mayumi and Zeng, Yan and Maeda, Takashi Nicholas and Shimizu, Shohei},
          journal={Journal of Machine Learning Research},
          volume={24},
          number={14},
          pages={1--8},
          year={2023}
        }

Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions

Feng Xie, Yan Zeng, Yangbo He, Zhengming Chen, Zhi Geng

Neurocomputing, 2023.

PDF

        @article{xie2023causal,
            title={Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions},
            author={Xie, Feng and Zeng, Yan and He, Yangbo and Chen, Zhengming and Geng, Zhi},
            journal={Neurocomputing},
            year={2023}
        }

Automatical Spike Sorting with Low-Rank and Sparse Representation

Libo Huang, Lu Gan, Yan Zeng, Bingo Wing-Kuen Ling^#

IEEE Transactions on Biomedical Engineering (IEEE TBE), 2023.

PDF

        @article{huang2023automatical,
            title={Automatical Spike Sorting with Low-Rank and Sparse Representation},
            author={Huang, Libo and Gan, Lu and Zeng, Yan and Ling, Bingo Wing-Kuen},
            journal={IEEE Transactions on Biomedical Engineering (IEEE TBE)},
            volume={71},
            number={5},
            pages={1677--1686},
            year={2023},
            publisher={IEEE}
        }

2022

Causal Discovery for Linear Mixed Data

Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun

Conference on Causal Learning and Reasoning (CleaR), 2022.

PDF Code

        @inproceedings{zeng2022causal,
          title={Causal Discovery for Linear Mixed Data},
          author={Zeng, Yan and Shimizu, Shohei and Matsui, Hidetoshi and Sun, Fuchun},
          booktitle={Conference on Causal Learning and Reasoning},
          pages={1018--1033},
          year={2022}
        }

Offline Causal Imitation Learning with Latent Confounders

Siyang Huang*, Yan Zeng*, Ruichu Cai, Zhifeng Hao, Fuchun Sun

International Conference on Cognitive Computation and Systems (CCIS), 2022.

PDF

    @inproceedings{huang2022offline,
        title={Offline Causal Imitation Learning with Latent Confounders},
        author={Huang, Siyang and Zeng, Yan and Cai, Ruichu and Hao, Zhifeng and Sun, Fuchun},
        booktitle={International Conference on Cognitive Computation and Systems},
        year={2022}
    }

2021

Causal Discovery with Multi-Domain LiNGAM for Latent Factors

Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao

International Joint Conference on Artificial Intelligence (IJCAI), 2021.

PDF Code

        @inproceedings{zeng2021causal,
            title={Causal discovery with multi-domain LiNGAM for latent factors},
            author={Zeng, Yan and Shimizu, Shohei and Cai, Ruichu and Xie, Feng and Yamamoto, Michio and Hao, Zhifeng},
            booktitle={International Joint Conference on Artificial Intelligence},
            year={2021}
        }

Nonlinear Causal Discovery with Multiple High-Dimensional Observations

Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, Shohei Shimizu

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2021.

PDF Code

    @article{zeng2021nonlinear,
        title={Nonlinear Causal Discovery with Multiple High-Dimensional Observations},
        author={Zeng, Yan and Hao, Zhifeng and Cai, Ruichu and Xie, Feng and Huang, Libo and Shimizu, Shohei},
        journal={IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)},
        volume={34},
        number={5},
        pages={2234--2245},
        year={2021},
        publisher={IEEE}
    }

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

Libo Huang, Lu Gan, Bingo Wing-Kuen Ling^#

IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNSRE), 2021. [JCR-Q1]

PDF Code

        @article{huang2021unified,
            title={A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting},
            author={Huang, Libo and Gan, Lu and Ling, Bingo Wing-Kuen},
            journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNSRE)},
            volume={29},
            pages={750--759},
            year={2021},
            publisher={IEEE}
        }

Before 2020

A Causal Discovery Algorithm Based on the Prior Selection of Leaf Nodes

Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Liang Ou, Ruihui Huang

Neural Networks, 2020.

PDF

        @article{zeng2020causal,
            title={A causal discovery algorithm based on the prior selection of leaf nodes},
            author={Zeng, Yan and Hao, Zhifeng and Cai, Ruichu and Xie, Feng and Ou, Liang and Huang, Ruihui},
            journal={Neural Networks},
            year={2020}
        }

An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables

Feng Xie, Ruichu Cai, Yan Zeng, Jiantao Gao, Zhifeng Hao

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.

PDF

    @article{xie2019efficient,
      title={An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables},
      author={Xie, Feng and Cai, Ruichu and Zeng, Yan and Gao, Jiantao and Hao, Zhifeng},
      journal={IEEE Transactions on Neural Networks and Learning Systems},
      year={2019}
    }

Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples

Feng Xie, Ruichu Cai, Yan Zeng, Zhifeng Hao

International Conference on Intelligence Science (IScIDE), Springer, 2019: 381-393.

PDF

    @inproceedings{xie2019causal,
      title={Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples},
      author={Xie, Feng and Cai, Ruichu and Zeng, Yan and Hao, Zhifeng},
      booktitle={International Conference on Intelligence Science},
      pages={381--393},
      year={2019}
    }

Spike Sorting Based On Low-Rank And Sparse Representation

Libo Huang^#, Bingo Wing-Kuen Ling, Yan Zeng, Lu Gan

IEEE International Conference on Multimedia and Expo (ICME), 2020

Oral PDF Slides

    @inproceedings{huang2020spike,
        title={Spike Sorting Based On Low-Rank And Sparse Representation},
        author={Huang, Libo and Ling, Bingo Wing-Kuen and Zeng, Yan and Gan, Lu},
        booktitle={IEEE International Conference on Multimedia and Expo (ICME)},
        pages={1--6},
        year={2020},
        organization={IEEE}
    }

WMsorting: Wavelet Packets' Decomposition and Mutual Information-based Spike Sorting Method

Libo Huang, Bingo Wing-Kuen Ling^#, Ruichu Cai^#, Yan Zeng, Jiong He, Yao Chen

IEEE Transactions on NanoBioscience (IEEE TNB), 2019

PDF

    @article{huang2019wmsorting,
        title={WMsorting: Wavelet Packets' Decomposition and Mutual Information-based Spike Sorting Method},
        author={Huang, Libo and Ling, Bingo Wing-Kuen and Cai, Ruichu and Zeng, Yan and He, Jiong and Chen, Yao},
        journal={IEEE Transactions on NanoBioscience (IEEE TNB)},
        volume={18},
        number={3},
        pages={283--295},
        year={2019},
        publisher={IEEE}
    }

Others

Academic Services

: Outstanding Reviewer

Conference Program Committee Member: NeurIPS (2024, 2025, 2026), ICML (2024, 2025, 2026), ICLR (2024, 2025), AAAI (2023, 2024, 2025, 2026, 2027), CVPR (2025), KDD (2025), ICDM (2024, 2026), ACM MM (2023), UAI (2022 , 2023, 2024, 2026), CLeaR (2022, 2023, 2024, 2025, 2026),
Journal Reviewer: JMLR (2026–present), IEEE TNNLS (2025–present), Science China Information Sciences (2024–present), IEEE Transactions on Cybernetics (2025–present), Knowledge-Based Systems (2026–present), Neural Networks (2025–present), Neurocomputing (2021–present), IEEE TAI (2025–present), IEEE TIE (2025–present), ACM TIST (2025–present), KAIS (2024–present),
Professional Service: CSIAM [中国工业与应用数学学会] (2025-), CAAI [中国人工智能学会] (2024-), CAAS [中国现场统计研究会] (2022-), CCF [中国计算机学会] (2017-),
Youth Editorial Board Member: CAAI AIR [CAAI Artificial Intelligence Research] (2026-)

Teaching

Distributed and Parallel Computing (Fall 2023, Fall 2024, Fall 2025, Fall 2026)
Probability Theory and Mathematical Statistics (Spring 2024, Fall 2025)
Probabilistic Foundations of Data Science (Fall 2024, Fall 2025, Fall 2026)
Mathematical Statistics (Spring 2026)
Frontiers in Statistics (Ph.D. Course, Co-taught) (Spring 2026)
Probability Theory (Fall 2026)

Yan Zeng (Yana)

About Me LinkedIn

Research Interests Team Website

News

Publications Google Scholar

2026

2025

2024

2023

2022

2021

Before 2020

Others

Academic Services

Teaching