Feng Xie
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Biography
I am currently an associate professor in the Department of Applied Statistics at Beijing Technology and Business University. Before joining BTBU, I did postdoctoral research in the Department of Probability and Statistics at Peking University from 2020 to 2022, working with Prof Zhi Geng and Prof Yangbo He. I obtained my PhD in the School of Computer Science at Guangdong University of Technology (2017 - 2020), supervised by Prof Ruichu Cai and co-supervised by Prof Kun Zhang (Carnegie Mellon University). I got my Master’s degree in the School of Mathematics and Statistics at Guangdong University of Technology (2014 - 2017), supervised by Prof Zhifeng Hao. From 2019 - 2020, I was a visiting Ph.D. student in the Department of Philosophy, Carnegie Mellon University.
Research Interests:
Causal Discovery, especially in latent variable models, causal factor analysis, and causal representation learning.
Instrumental Variable Model, especially in discovering valid IVs from observational data.
I'm looking for students. Welcome to contact me if you are interested!
Publications (* Equal Contribution, # Corresponding author) [Google Scholar Link]
Preprints
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Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models.
Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie#.
arXiv:2411.12184, 2024. -
Assessing the causes of continuous effects by posterior effects of causes.
Shanshan Luo, Yixuan Yu, Chunchen Liu, Feng Xie#, and Zhi Geng.
arXiv:2404.05246, 2024.
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Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments.
Feng Xie, Zhen Yao, Lin Xie, Yan Zeng, and Zhi Geng.
NeurIPS, Vancouver, Canada, 2024. (Oral) -
Learning Discrete Latent Variable Structures with Tensor Rank Conditions.
Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang.
NeurIPS, Vancouver, Canada, 2024. -
Identification and estimation of causal effects using non-Gaussianity and auxiliary covariates.
Kang Shuai, Shanshan Luo, Yue Zhang, Feng Xie, and Yangbo He.
To appear in Statistica Sinica, 2024. -
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables.
Feng Xie*, Biwei Huang*, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, and Kun Zhang.
JMLR, 25 (2024): 1-61. Local Causal Structure Learning in the Presence of Latent Variables.
Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, and Zhi Geng.
ICML, Vienna, Austria, 2024.Automating the Selection of Proxy Variables of Unmeasured Confounders.
Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng.
ICML, Vienna, Austria, 2024. (Spotlight)Policy Learning for Balancing Short-Term and Long-Term Rewards.
Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, and Yan Zeng.
ICML, Vienna, Austria, 2024.-
Testing Conditional Independence Between Latent Variables by Independence Residuals.
Zhengming Chen, Jie Qiao, Feng Xie, Ruichu Cai, Zhifeng Hao, Keli Zhang.
IEEE Transactions on NNLS, 2024. -
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability.
Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang.
ICLR, 2024. -
Identification of Nonlinear Latent Hierarchical Models.
Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang.
NeurIPS, 2023. Some General Identification Results for Linear Latent Hierarchical Causal Structure.
Zhengming Chen*, Feng Xie*, Jie Qiao, Zhifeng Hao, and Ruichu Cai.
IJCAI, 2023.Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions.
Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang.
Neurocomputing, 2023.Identification of Linear Non-Gaussian Latent Hierarchical Structure.
Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, and Kun Zhang.
ICML, Baltimore, Maryland USA, 2022. [code] (Spotlight)Latent Hierarchical Causal Structure Discovery with Rank Constraints.
Biwei Huang, Charles Low, Feng Xie, Clark Glymour, Kun Zhang.
NeurIPS, 2022.Identification of Linear Latent Variable Model with Arbitrary Distribution.
Z. Chen*, Feng Xie*, Jie Qiao*, Zhifeng Hao, Kun Zhang, and Ruichu Cai.
AAAI, Vancouver, CANADA, 2022.Testability of Instrument Validity in Linear non-Gaussian Acyclic Causal Models.
Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, and Kun Zhang.
Entropy, 2022, 24(4), 512.Nonlinear causal discovery for high-dimensional deterministic data.
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, and Shohei Shimizu.
IEEE Transactions on NNLS, 2021, 34(5), 2234 - 2245.Causal discovery with multi-domain LiNGAM for latent factors.
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, and Zhifeng Hao.
IJCAI, Montreal-themed Virtual Reality, 2021.An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables.
Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao.
IEEE Transactions on NNLS, 2020, 31(5): 1667-1680.Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs.
Feng Xie*, Ruichu Cai*, Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang*.
NeurIPS, Virtual Conference, 2020.[Matlab code][Python code] (Spotlight)A causal discovery algorithm based on the prior selection of leaf nodes.
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Liang Ou, and Ruihui Huang.
Neural Networks, 2020, 124, 130-145.Mining hidden non-redundant causal relationships in online social networks.
Wei Chen, Ruichu Cai, Zhifeng Hao, Chang Yuan, and Feng Xie.
Neural Computing and Applications, 2020, 32, 6913-6923.Triad Constraints for Causal Discovery in the Presence of Latent Variables.
Ruichu Cai*, Feng Xie*, Clark Glymour, Zhifeng Hao, and Kun Zhang.
NeurIPS, Vancouver, CANADA, 2019. [Matlab code]Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples.
Feng Xie, Ruichu Cai, Yan Zeng, and Zhifeng Hao.
IScIDE 2019, 2019.An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data.
Ruichu Cai, Feng Xie, Wei Chen, Zhifeng Hao.
IWQoS, 2017.
Students
Zeyu Liu (2024-)
Fengtian Zhang (2024-)
Fuyan Huang (2024-)
Ze Wang (2024-)
Xichen Guo (2023-)
Zhen Yao (2023-)
Zheng Li (2023-)
Ni Yan (2023-; Co-supervision with Yan Zeng)
Yixuan Yu (2022-2024; Co-supervision with Xueli Wang)
Lin Xie
Teaching