Feng Xie 
Associate Professor
Department of Applied Statistics
& Causal Inference Group
Beijing Technology and Business University, Beijing, China. 102488.
Office: Room 209, Shutong Building, Higher Education Garden, Liangxiang
Email: fengxie@btbu.edu.cn
Biography
I am currently an Associate Professor in the Department of Applied Statistics at Beijing Technology and Business University (BTBU). Before joining BTBU, I was a postdoctoral researcher in the Department of Probability and Statistics at Peking University from 2020 to 2022, where I worked with Prof. Zhi Geng and Prof. Yangbo He. I received my Ph.D. from the School of Computer Science, Guangdong University of Technology (2017–2020), supervised by Prof. Ruichu Cai and co-supervised by Prof. Kun Zhang (Carnegie Mellon University). Prior to that, I obtained my Master's degree from the School of Mathematics and Statistics, Guangdong University of Technology (2014–2017) under the supervision of Prof. Zhifeng Hao. I was a visiting scholar at Carnegie Mellon University from 2019 to 2020 and at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) from February to July 2026.
Research Interests
Causal Structure Learning
Global and local causal structure learning under latent confounding, with emphasis on multi-source, interventional, and partially observed data.
Causal Effect Estimation
Selecting valid adjustment sets, instrumental variables, and proxy variables from observational data.
Causal Attribution
Causal structure-based attribution and root cause analysis for understanding how causes contribute to observed outcomes.
计划招收2026级硕士研究生若干名，欢迎对本研究方向感兴趣的同学发送简历至我的邮箱进一步沟通!
Publications (* Equal Contribution, 📧 Corresponding author) Google Scholar
Preprints
- Yan Zeng, Shenglan Nie, Feng Xie 📧, Libo Huang, Peng Wu, and Zhi Geng. Confounded Causal Imitation Learning with Instrumental Variables. arXiv:2507.17309, 2025. [PDF]
Published Papers
2026
- Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie 📧. Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models. Journal of Machine Learning Research, 2026. [PDF]
- Zeyu Liu, Zheng Li, Feng Xie 📧, Yan Zeng 📧, Hao Zhang, and Kun Zhang. Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions. ICML , Seoul, South Korea, 2026 (Spotlight, TOP 2.2%). [PDF]
- Zheng Li, Feng Xie 📧, Shenglan Nie, Xichen Guo, Ruxin Wang 📧, Hao Zhang 📧. A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables. ICML , Seoul, South Korea, 2026 (Oral, TOP 0.7%). [PDF]
- Yixin Ren, Hao Zhang, Yewei Xia, Feng Xie, Jihong Guan, Shuigeng Zhou. Causal Discovery by Multi-Level Wavelet Mapping Correlation Based Statistical Dependence Measurement. ACM Transactions on Knowledge Discovery from Data (TKDD) , 2026, Accepted. [PDF]
2025
- Zheng Li, Xichen Guo, Feng Xie 📧, Yan Zeng, Hao Zhang 📧, and Zhi Geng. Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables. NeurIPS , San Diego Convention Center, 2025. [PDF]
- Yixin Ren, Juncai Zhang, Yewei Xia, Ruxin Wang, Feng Xie, Jihong Guan, Hao Zhang, and Shuigeng Zhou. Regression-based Conditional Independence Test with Adaptive Kernels. Artificial Intelligence, 2025. [PDF]
- Zheng Li, Zeyu Liu, Feng Xie 📧, Hao Zhang, Chunchen Liu, and Zhi Geng. Local Identifying Causal Relations in the Presence of Latent Variables. ICML, Vancouver, Canada, 2025 (Spotlight, TOP 2.6%). [PDF]
- Xichen Guo, Feng Xie 📧, Yan Zeng, Hao Zhang, and Zhi Geng. Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models. ICML, Vancouver, Canada, 2025. [PDF]
- Shanshan Luo, Yixuan Yu, Chunchen Liu, Feng Xie 📧, and Zhi Geng. Causal Attribution Analysis for Continuous Outcomes. ICML, Vancouver, Canada, 2025 (Spotlight, TOP 2.6%). [PDF]
- Zhengming Chen, Yewei Xia, Feng Xie, Jie Qiao, Zhifeng Hao, Ruichu Cai, and Kun Zhang. Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations. ICML, Vancouver, Canada, 2025. [PDF]
- Yewei Xia, Xueliang Cui, Hao Zhang, Yixin Ren, Feng Xie, Jihong Guan, Ruxin Wang, and Shuigeng Zhou. Identifying Causal Mechanism Shifts under Additive Models with Arbitrary Noise. IJCAI, Montreal, Canada, 2025. [PDF]
2024
- Feng Xie, Zhen Yao, Lin Xie, Yan Zeng, and Zhi Geng. Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments. NeurIPS, Vancouver, Canada, 2024 (Oral, TOP 0.39%). [PDF]
- Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, and Kun Zhang. Learning Discrete Latent Variable Structures with Tensor Rank Conditions. NeurIPS, Vancouver, Canada, 2024. [PDF]
- Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He. Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates. Statistica Sinica, 2024. [PDF]
- Feng Xie* , Biwei Huang*, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhifeng Hao, and Kun Zhang. Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. Journal of Machine Learning Research, 2024. [Python code] [PDF]
- Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, and Zhi Geng. Local Causal Structure Learning in the Presence of Latent Variables. ICML, Vienna, Austria, 2024. [PDF] [Python code]
- Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, and Zhi Geng. Automating the Selection of Proxy Variables of Unmeasured Confounders. ICML, Vienna, Austria, 2024 (Spotlight, TOP 1.98%). [PDF] [Python code]
- Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, and Yan Zeng. Policy Learning for Balancing Short-Term and Long-Term Rewards. ICML, Vienna, Austria, 2024. [PDF]
- Zhengming Chen, Jie Qiao, Feng Xie, Ruichu Cai, Zhifeng Hao, and Keli Zhang. Testing Conditional Independence Between Latent Variables by Independence Residuals. IEEE Transactions on Neural Networks and Learning Systems, 2024. [PDF]
- Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, and Kun Zhang. Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability. ICLR, 2024. [PDF]
2023
- Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, and Kun Zhang. Identification of Nonlinear Latent Hierarchical Models. NeurIPS, 2023. [PDF]
- Zhengming Chen* , Feng Xie* , Jie Qiao, Zhifeng Hao, and Ruichu Cai. Some General Identification Results for Linear Latent Hierarchical Causal Structure. IJCAI, 2023. [PDF]
- Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang. Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions. Neurocomputing, 2023. [PDF]
Before 2023
- Biwei Huang, Charles Low, Feng Xie, Clark Glymour, and Kun Zhang. Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS, 2022. [PDF]
- Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang. Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML, Baltimore, MD, USA, 2022 (Spotlight). [PDF]
- Z. Chen*, Feng Xie*, Jie Qiao*, Zhifeng Hao, Kun Zhang, and Ruichu Cai. Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI, 2022. [PDF]
- Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, and Kun Zhang. Testability of Instrument Validity in Linear non-Gaussian Acyclic Causal Models. Entropy, 2022, 24(4), 512. [PDF]
- Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, and Shohei Shimizu. Nonlinear causal discovery for high-dimensional deterministic data. IEEE Trans. on Neural Networks and Learning Systems, 2021, 34(5): 2234–2245. [PDF]
- Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, and Zhifeng Hao. Causal discovery with multi-domain LiNGAM for latent factors. IJCAI, 2021. [PDF]
- Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables. IEEE Trans. on Neural Networks and Learning Systems, 2020, 31(5): 1667–1680. [PDF]
- Feng Xie* , Ruichu Cai* , Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang*. Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS, 2020 (Spotlight, TOP 2.96%). [PDF] [Matlab code] [Python code]
- Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Liang Ou, and Ruihui Huang. A causal discovery algorithm based on the prior selection of leaf nodes. Neural Networks, 2020, 124: 130–145. [PDF]
- Wei Chen, Ruichu Cai, Zhifeng Hao, Chang Yuan, and Feng Xie. Mining hidden non-redundant causal relationships in online social networks. Neural Computing and Applications, 2020, 32: 6913–6923. [PDF]
- Ruichu Cai* , Feng Xie* , Clark Glymour, Zhifeng Hao, and Kun Zhang. Triad Constraints for Causal Discovery in the Presence of Latent Variables. NeurIPS, 2019. [PDF] [Matlab code]
- Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao. Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples. IScIDE, 2019. [PDF]
- Ruichu Cai, Feng Xie, Wei Chen, and Zhifeng Hao. An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data. IWQoS, 2017. [PDF]
Students
PhD
- Xichen Guo (2025-; Co-supervision with Zhi Geng)
Master
- Lulu Xiang (2025-)
- Xingyu Liu (2025-)
- Zhiqiang Lai (2025-; Co-supervision with Yan Zeng)
- Keli Zhou (2025-; Co-supervision with Yan Zeng)
- Weicong Cheng (2025-)
- Zeyu Liu (2024-)
- Fengtian Zhang (2024-)
- Shenglan Nie (2024-; Co-supervision with Yan Zeng)
- Fuyan Huang (2024-)
- Ze Wang (2024-)
Undergraduate
- Yuting Zhang (2024-)
Alumni
- Xichen Guo (2023-2025; -> Phd at Beijing Technology and Business University, Beijing)
- Zhen Yao (2023-2025; -> Phd at Dalian University of Technology, Dalian)
- Zheng Li (2023-2025; -> Research Assistant at SIAT, Chinese Academy of Sciences; -> Phd at Fudan University, Shanghai)
- Lin Xie (2023-2025; -> Master at Sun Yat-sen University, Guangzhou)
- Yixuan Yu (2022-2024; -> Data Analyst in the Gaming Industry, Beijing)
Teaching
- Machine Learning (Fall 2026, Fall 2025, Fall 2024, Fall 2023)
- Data Analysis Based on Python (Spring 2025)
- Statistical Computing (Fall 2025, Spring 2025, Spring 2024)
- Applied Stochastic Processes (Fall 2022)
Academic Service
- Area Chair: NeurIPS 2024, 2025; ICML 2025, 2026.
- Conference PC or Reviewer: NeurIPS 2021,2022, 2023; ICML 2022, 2024; ICLR 2023, 2024; AAAI 2025; KDD 2025; AISTATS 2022, 2023, 2024, 2026; UAI 2022, 2024; CLeaR 2022, 2023, 2024.
- Journal Reviewer: IEEE Transactions on PAMI (TPAMI); Journal of the American Statistical Association (JASA); Journal of Machine Learning Research (JMLR); IEEE Transactions on NNLS (TNNLS); Transactions on KDD (TKDD); Transactions on MLR (TMLR); IEEE Journal on Selected Areas in Information Theory; Information Science, Science China: Information Sciences and so on.