Attuned to Change: Causal Fine-Tuning under Latent-Confounded Shifts

• URL: http://arxiv.org/abs/2410.14375v2
• Date: Thu, 12 Jun 2025 20:01:43 GMT
• Title: Attuned to Change: Causal Fine-Tuning under Latent-Confounded Shifts
• Authors: Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Junchi Yu, Philip Torr, Ricardo Silva,
• Abstract summary: Adapting to latent-confounded shifts remains a core challenge in modern AI.<n>One practical failure mode arises when fine-tuning pre-trained foundation models on confounded data.<n>We frame causal fine-tuning as an identification problem and pose an explicit causal model that decomposes inputs into low-level spurious features.
• Score: 32.989526411946606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapting to latent-confounded shifts remains a core challenge in modern AI. These shifts are propagated via latent variables that induce spurious, non-transportable correlations between inputs and labels. One practical failure mode arises when fine-tuning pre-trained foundation models on confounded data (e.g., where certain text tokens or image backgrounds spuriously correlate with the label), leaving models vulnerable at deployment. We frame causal fine-tuning as an identification problem and pose an explicit causal model that decomposes inputs into low-level spurious features and high-level causal representations. Under this family of models, we formalize the assumptions required for identification. Using pre-trained language models as a case study, we show how identifying and adjusting these components during causal fine-tuning enables automatic adaptation to latent-confounded shifts at test time. Experiments on semi-synthetic benchmarks derived from real-world problems demonstrate that our method outperforms black-box domain generalization baselines, illustrating the benefits of explicitly modeling causal structure.

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