Publication

Research interests

My research is mainly focused on causal inference, with particular interests in

• Synthetic data, the generation and use of them in causal settings;
• Sequential modeling and anytime valid inference, particularly e-processes;
• Causal data simulation using deep generative models;
• Data fusion techniques for integrating heterogeneous sources;
• Distributional learning frameworks for inference under changing environments.

Please get in touch for collaborations.

Selected papers

Please find my Google Scholar for the full list.

Data Fusion with Distributional Equivalence Test-then-pool
Linying Yang, Xing Liu, Robin J. Evans (2026)
arXiv preprint — Paper 

Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Linying Yang, Robin J. Evans, Xinwei Shen (2025)
arXiv preprint — Paper | Package | Code for reproducibility | Post

Outcome‑Informed Weighting for Robust ATE Estimation
Linying Yang, Robin J. Evans (2025)
arXiv preprint — Paper | Code | Post

Testing Generalizability in Causal Inference
Daniel de Vassimon Manela*,  Linying Yang*,  Robin J. Evans (2025)
41st Conference on Uncertainty in Artificial Intelligence (UAI 2025) — Paper | Code

The Development and Deployment of a Model for Hospital‑Level COVID‑19 Associated Patient Demand Intervals from Consistent Estimators (DICE)
Linying Yang, Teng Zhang, Peter Glynn, David Scheinker (2021)
Health Care Management Science (2021) — Paper