Welcome to my research repository! 📚 My work centers on local causal discovery and inference, particularly in scenarios involving latent variables. I am interested in developing methods that uncover hidden causal relationships and enable robust statistical inference, even when some variables are unobserved. These approaches have broad applications in machine learning and statistics, including improving model interpretability, enhancing causal effect estimation, and advancing the understanding of complex data-generating processes.
- Causal discovery in the presence of latent variables 🕵️♂️
- Selecting valid adjustment sets for causal effect estimation 🧮
- Testability of instrumental variables 🧪
- Testability of instrumental variables in additive nonlinear, non-constant effects models
X. Guo*, Z. Li*, B. Huang, Y. Zeng, Z. Geng, F. Xie
arXiv preprint arXiv:2411.12184
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Local causal structure learning in the presence of latent variables
F. Xie, Z. Li, P. Wu, Y. Zeng, C. Liu, Z. Geng
ICML 2024 -
Local Identifying Causal Relations in the Presence of Latent Variables
Z. Li*, Z. Liu*, F. Xie, H. Zhang, C. Liu, Z. Geng
ICML 2025, Spotlight, TOP 2.6% -
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Z. Li, X. Guo, F. Xie, Y. Zeng, H. Zhang, Z. Geng
NeurIPS 2025
- Email: [zhengli0060(at)gmail(dot)com]