Time: July 23, 2026, Thursday, 4:00–5:00 p.m.

Moderator: Professor Zhongyi Zhu, Department of Statistics and Data Science

Venue: Guoshun Campus, Room 302, Starr Building

Speaker: Professor Annie Qu, University of California, Santa Barbara

Title: Transferring Treatment Effects Across Heterogeneous Sites via Distributional Causal Inference

Abstract: We propose a novel framework for synthesizing counterfactual treatment group data in a target site by integrating full treatment and control group data from a source site with control group data from the target. Departing from conventional average treatment effect estimation, our approach adopts a distributional causal inference perspective by modeling treatment and control as distinct probability measures on the source and target sites. We formalize the cross-site heterogeneity (effect modification) as a push-forward transformation that maps the joint feature - outcome distribution from the source to the target site. This transformation is learned by aligning the control group distributions between sites using an Optimal Transport - based procedure, and subsequently applied to the source treatment group to generate the synthetic target treatment distribution. Under general regularity conditions, we establish theoretical guarantees for the consistency and asymptotic convergence of the synthetic treatment group data to the true target distribution. Simulation studies across multiple data-generating scenarios and a real-world application to patient derived xenograft data demonstrate that our framework robustly recovers the full distributional properties of treatment effects.

Bio: Professor Annie Qu is Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara, and Founding Director of the Center for Statistical Foundations of Artificial Intelligence. Her research focuses on foundational issues in the analysis of large-scale, complex structured and unstructured data. She develops cutting-edge statistical methods and theories for complex and heterogeneous data, with research interests spanning machine learning, precision medicine algorithms, text mining, recommender systems, medical imaging data analysis, and network data analysis.From 2008 to 2019, she was a faculty member at the University of Illinois Urbana-Champaign, where she served as Founding Professor of Data Science and Director of the Illinois Statistics Office. From 2020 to 2025, she was Chancellor's Professor at the University of California, Irvine. Professor Qu received the U.S. National Science Foundation (NSF) CAREER Award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and the American Association for the Advancement of Science. She received the IMS Medallion Award in 2024 and was invited to serve as an IMS Medallion Lecturer. From 2023 to 2025, she served as Co-Editor of the Theory and Methods section of the Journal of the American Statistical Association (JASA). She serves as IMS Program Secretary from 2021 to 2027 and served as Council of Sections Governing Board Chair of the American Statistical Association (ASA) in 2025. Her other honors include the 2025 IMS Carver Medal and the 2026 International Chinese Statistical Association (ICSA) Distinguished Achievement Award.