I am a Computer Science student at Guangdong University of Technology. My current interests lie at the intersection of causal effect estimation, in-context learning, and foundation models for tabular data.

• 🎓 B.Sc. in Computer Science and Technology, 2022–2026
• 📚 Incoming M.S. student at Guangdong University of Technology, 2026–2029
• 🔬 Interested in treatment effect estimation, causal sensitivity analysis, PFNs, TabPFN, and tabular foundation models
• 💻 Developing research-oriented machine learning systems with Python and PyTorch

• Causal effect estimation: average treatment effects, CATE/HTE estimation, potential outcomes, and observational studies
• Causal sensitivity analysis: robustness to unmeasured confounding and partial-identification bounds
• Prior-Data Fitted Networks: PFNs, TabPFN, amortized inference, and in-context learning
• Tabular foundation models: representation learning and foundation models for structured data
• Synthetic causal data: structural causal models, DAG generation, treatment mechanisms, and counterfactual outcomes

• Building PFN-style models for estimating heterogeneous treatment effects from tabular observational data
• Studying causal sensitivity methods for obtaining valid upper and lower bounds under hidden confounding
• Designing synthetic causal data generators with explicit covariate roles, treatment mechanisms, and potential outcomes
• Training and evaluating deep learning models with distributed PyTorch workflows

causal effect estimation · treatment effect estimation · heterogeneous treatment effects · CATE · causal sensitivity analysis · Prior-Data Fitted Networks · TabPFN · in-context learning · tabular foundation models