Publication

Research interests

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

• Distributional shift analysis, including methods to handle positivity violations;
• Causal data simulation using deep generative models;
• Data fusion techniques for integrating heterogeneous sources;
• Distributional learning frameworks for inference under changing environments;
• Robustness and sensitivity analysis in causal models.

I am currently seeking postdoctoral positions beginning in 2026. Please get in touch for collaborations.

Selected papers

Please find my Google Scholar for the full list.

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