Description
Dr. Larry Han
Assistant Professor of Biostatistics, Department of Public Health and Health Sciences
Northeastern University
“On the Role of Surrogates in Conformal Inference of Individual Causal Effects”
Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can provide valid uncertainty quantification for ITEs, the resulting prediction intervals are often excessively wide, limiting their practical utility. To address this limitation, we introduce Surrogate-assisted Conformal Inference for Efficient INdividual Causal Effects (SCIENCE), a framework designed to construct more efficient prediction intervals for ITEs. SCIENCE accommodates the covariate shifts between source data and target data and applies to various data configurations, including semi-supervised and surrogate-assisted semi-supervised learning. Leveraging semi-parametric efficiency theory, SCIENCE produces rate double-robust prediction intervals under mild rate convergence conditions, permitting the use of flexible non-parametric models to estimate nuisance functions. We quantify efficiency gains by comparing sem-parametric efficiency bounds with and without the surrogates. Simulation studies demonstrate that our surrogate-assisted intervals offer substantial efficiency improvements over existing methods while maintaining valid group-conditional coverage. Applied to the phase 3 Moderna COVE COVID-19 vaccine trial, SCIENCE illustrates how multiple surrogate markers can be leveraged to generate more efficient prediction intervals.
Biography
Larry Han is an assistant professor in the Department of Public Health and Health Sciences at Northeastern University and an Affiliate Investigator in the Vaccine and Infectious Disease Division at the Fred Hutch Cancer Center. His research focuses on developing novel statistical and machine learning methods to leverage real-world data to improve decision-making in public health and clinical medicine. This involves designing robust, efficient and targeted estimators for causal effects using large-scale data generated from electronic health records and clinical trial data. Active areas of research include causal inference, conformal inference, data integration, federated and transfer learning and sensitivity analysis. He obtained a Ph.D. in Biostatistics at Harvard University, advised by Professor Tianxi Cai and Professor Lorenzo Trippa and completed a postdoctoral fellowship in Health Care Policy at Harvard Medical School, advised by Professor Sharon-Lise Normand.
Featuring: Dr. Larry Han, Assistant Professor of Biostatistics, Northeastern University
Location
Online
Category:
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