3 papers
cs.LG2026
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
stat.ML2026
Doubly robust identification of treatment effects from multiple environments
Piersilvio De Bartolomeis, Julia Kostin, Javier Abad +2
Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are pr…
stat.ML2025
Achievable distributional robustness when the robust risk is only partially identified
Julia Kostin, Nicola Gnecco, Fanny Yang
In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk. Invariance-based algorithms…