提出新框架解决标签生成特征的反向因果问题
Measure-Theoretic Anti-Causal Representation Learning
- 基于测度论构建双层表征，分层捕捉生成机制与稳定模式
- 在真实医疗数据上准确率与不变性均超越现有方法
- 无需显式因果结构，支持理想与非理想干预，理论可保证泛化
反向因果场景（标签导致特征）中的因果表征学习面临独特挑战，需专门方法。我们提出反向因果不变抽象（ACIA），一种新颖的测度论框架。该框架采用双层设计：低层表征捕捉标签如何生成观测，高层表征学习跨环境变化的稳定因果模式。ACIA通过干预核处理理想与非理想干预，不依赖显式因果结构，有效处理高维数据，并提供分布外泛化的理论保障。在合成数据和真实医疗数据集上的实验表明，ACIA在准确率和不变性指标上持续优于现有最优方法。理论分析建立了训练与未见环境间性能差距的紧致上界，验证了方法在鲁棒反向因果学习中的有效性。
原文摘要 · Abstract (English)
Causal representation learning in the anti-causal setting (labels cause features rather than the reverse) presents unique challenges requiring specialized approaches. We propose Anti-Causal Invariant Abstractions (ACIA), a novel measure-theoretic framework for anti-causal representation learning. ACIA employs a two-level design, low-level representations capture how labels generate observations, while high-level representations learn stable causal patterns across environment-specific variations. ACIA addresses key limitations of existing approaches by accommodating prefect and imperfect interventions through interventional kernels, eliminating dependency on explicit causal structures, handling high-dimensional data effectively, and providing theoretical guarantees for out-of-distribution generalization. Experiments on synthetic and real-world medical datasets demonstrate that ACIA consistently outperforms state-of-the-art methods in both accuracy and invariance metrics. Furthermore, our theoretical results establish tight bounds on performance gaps between training and unseen environments, confirming the efficacy of our approach for robust anti-causal learning.
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