Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings

• URL: http://arxiv.org/abs/2602.19788v1
• Date: Mon, 23 Feb 2026 12:44:22 GMT
• Title: Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
• Abstract summary: We propose a causally-aware Bayesian meta-learning method, by conditioning task-specific priors on latent causal task embeddings.<n>We show that conditioning on causal embeddings controls prior mismatch and mitigates negative transfer under task shift.
• Score: 21.675126545912423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meta-learning methods perform well on new within-distribution tasks but often fail when adapting to out-of-distribution target tasks, where transfer from source tasks can induce negative transfer. We propose a causally-aware Bayesian meta-learning method, by conditioning task-specific priors on precomputed latent causal task embeddings, enabling transfer based on mechanistic similarity rather than spurious correlations. Our approach explicitly considers realistic deployment settings where access to target-task data is limited, and adaptation relies on noisy (expert-provided) pairwise judgments of causal similarity between source and target tasks. We provide a theoretical analysis showing that conditioning on causal embeddings controls prior mismatch and mitigates negative transfer under task shift. Empirically, we demonstrate reductions in negative transfer and improved out-of-distribution adaptation in both controlled simulations and a large-scale real-world clinical prediction setting for cross-disease transfer, where causal embeddings align with underlying clinical mechanisms.

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