CEH Webinar 3/26/2026 Gabriel Zayas-Cabán

Title: 

Designing a quasi-experiment to study the clinical impact of adaptive risk prediction models

Abstract: 

Clinical risk prediction is a valuable tool for guiding healthcare interventions toward those most likely to benefit. Yet, evaluating the pairing of a risk prediction model with an intervention using randomized controlled trials presents substantial challenges, making quasi-experimental designs an attractive alternatives. Existing designs, however, assume that both the model and the decision rules used to trigger interventions (typically a risk threshold) remain fixed. This limits their utility in modern healthcare, where both are routinely updated. We introduce a regression discontinuity framework that accommodates adaptation in both the model and the risk threshold. We precisely characterize the form of interference introduced by these adaptations and exploit this structure to establish conditions for identification and thus design estimation strategies. The key idea is to define counterfactual risks-the scores patients would have received under hypothetical reorderings-thereby restoring local exchangeability and enabling valid estimation of the local average treatment effect. Our estimator leverages the fact that, although counterfactual risk vectors grow with time, they typically lie in a low-dimensional space. In simulations of cardiovascular prevention programs, we show that our method accurately recovers treatment effects even as thresholds adapt to meet operational or clinical targets and models are updated to align predicted and observed outcomes or to exclude demographic predictors such as race. 

Bio: 

Gabriel Zayas-Cabán is an Associate Professor in the Industrial and Systems Engineering Department at the University of Wisconsin-Madison. He also holds an affiliate appointment with the BerbeeWalsh Department of Emergency Medicine in the School of Medicine and Public Health. Before coming to Wisconsin, Gabriel was a President's Postdoctoral Fellow at the University of Michigan and completed his Ph.D. at Cornell University's Center for Applied Mathematics.
His work spans healthcare delivery, stochastic modeling and optimization, and causal inference. His recent focus is on causal inference methods to evaluate the use of predictive models in healthcare. He has won several awards, including the INFORMS MIF Early Career Award. He is the recipient of an ICTR KL2 Career Development Award and has additional funding from the Robert Wood Johnson Foundation (RWJ) and the Health Resources and Services Administration (HRSA).

Add Event To My Group

Please sign-in