Research

Research Interests

My research interests focus on developing statistical tools for robust causal inference and data-driven decision-making in social and biomedical sciences. Specifically, I have been working on

Policy learning in causal inference
Heterogeneous treatment effect estimation
Transfer Learning and Federated Learning

Working Papers and Publications

Individualized Policy Evaluation and Learning under Clustered Network Interference
Yi Zhang, Kosuke Imai. (2023)
2024 JSM Best Student Paper Award in Business and Economic Statistics Section
[arXiv]

Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs
Yi Zhang, Eli Ben-Michael, Kosuke Imai. (2022)
2023 JSM Best Student Paper Award in Social Statistics Section
[arXiv]

AI and Generative AI for Research Discovery and Summarization
Mark Glickman, Yi Zhang. (2024)
Harvard Data Science Review, 2024
[arXiv][Journal]

A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery
Larry Han, Yi Zhang, Meena Nathan, John E. Mayer, Jr., Sara K. Pasquali, Katya Zelevinsky, Rui Duan, Sharon-Lise T. Normand. (2024)
[arXiv]

Heterogeneous Causal Effect Estimation in Underrepresented Populations: Federated and Transfer Learning Approaches
Larry Han, Yi Zhang, Rui Duan. In preparation.

Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models
Molei Liu*, Yi Zhang*, Katherine P Liao, Tianxi Cai. (2022)
Journal of Machine Learning Research, 2023
[arXiv][Journal]

Double/Debiased Machine Learning for Logistic Partially Linear Model
Molei Liu*, Yi Zhang*, Doudou Zhou*. (2020)
The Econometrics Journal, 2021
[arXiv][Code][Journal]

*: The authors contributed equally to this work or are listed in alphabetic order.