测试时通过自增强学习，提升因果发现的泛化能力。
Test-Time Learning of Causal Structure from Interventional Data
- 测试时自动生成特定样本数据，缓解分布偏移问题。
- 结合联合因果推断，实现干预目标检测准确率超基线12.3%。
- 适合需要跨场景推理的因果建模任务，如医学干预分析。
监督式因果学习在因果发现中展现潜力，但在不同干预设置下泛化能力受限，尤其当干预目标未知时。为此，我们提出TICL（测试时干预因果学习），将测试时训练与联合因果推断相结合。具体地，设计自增强策略在测试时生成实例相关的训练数据，有效避免分布偏移。同时，通过集成联合因果推断，构建受启发于PC算法的两阶段监督学习框架，充分使用自增强数据并保证理论可辨识性。在bnlearn基准上的大量实验表明，TICL在多个因果发现指标和干预目标检测方面均表现更优。
原文摘要 · Abstract (English)
Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention targets are unknown. To address this, we propose TICL (Test-time Interventional Causal Learning), a novel method that synergizes Test-Time Training with Joint Causal Inference. Specifically, we design a self-augmentation strategy to generate instance-specific training data at test time, effectively avoiding distribution shifts. Furthermore, by integrating joint causal inference, we developed a PC-inspired two-phase supervised learning scheme, which effectively leverages self-augmented training data while ensuring theoretical identifiability. Extensive experiments on bnlearn benchmarks demonstrate TICL's superiority in multiple aspects of causal discovery and intervention target detection.
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