5 papers
Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence
Andreas Koukorinis, Ricardo Silva
We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.
Causal Fine-Tuning under Latent Confounded Shift
Jialin Yu, Yuxiang Zhou, Haoxuan Li +6
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs d…
A Shift in Perspective on Causality in Domain Generalization
Damian Machlanski, Stephanie Riley, Edward Moroshko +7
The promise that causal modelling can lead to robust AI generalization has been challenged in recent work on domain generalization (DG) benchmarks. We revisit the claims of the cau…
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
Kaican Li, Weiyan Xie, Yongxiang Huang +5
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. H…
Structured Learning of Compositional Sequential Interventions
Jialin Yu, Andreas Koukorinis, Nicolò Colombo +2
We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as "clo…