Prof. Falco J. Bargagli Stoffi, an Assistant Professor in the Department of Biostatistics at the Fielding School of Public Health at UCLA, will be speaking about "Causal Stability Selection" on Wednesday, October 7th, 2026 from 3:30 - 4:30pm in HSSB 1173.
Abstract:
Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the expected number of false discoveries, risking spurious findings that fail to replicate. We introduce causal stability selection, an algorithm that combines cross-fitted estimation of conditional average treatment effects with integrated path stability selection. The method accommodates arbitrary treatment effect estimators and arbitrary base selectors, and produces a selection set with an explicit, non-asymptotic bound on the expected number of false positives. Under standard causal identifying assumptions and regularity conditions on the base selector, we prove that the estimated selection probabilities converge to their oracle counterparts at the rate of the underlying treatment effect estimator. This establishes a direct connection between treatment effect estimation and effect modifier discovery. We illustrate the method on simulated data and on real-world data from a randomized oncology trial.
Bio:
Falco Bargagli Stoffi is an Assistant Professor in the Department of Biostatistics at the Fielding School of Public Health. Previously, he was a researcher at Harvard University. His research interests are primarily in methodological and applied (bio)statistics with a focus on applications of causal inference and machine learning in public health and medicine.