MetaCaDI用元学习识别未知干预，少样本下仍能准确找病因。
MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
- 将未知干预识别建模为元学习问题，共享因果图提升泛化能力。
- 仅需3个样本即可准确识别干预目标，现有方法在此时已随机猜测。
- 解析式自适应避免复杂优化，适合小样本、多环境的因果发现任务。
揭示复杂现实系统中的因果机制仍面临挑战，因数据收集成本高且干预方式未知。我们提出MetaCaDI，首个将未知干预识别建模为元学习问题的框架，显式利用联合学习的因果图。该贝叶斯框架在多个环境中学习共享因果结构，并可快速适应新的少样本干预目标识别任务。关键创新在于解析式自适应，通过闭式解规避昂贵且不稳定的梯度双层优化。在合成数据和复杂基因表达数据上的大量实验表明，MetaCaDI显著优于现有最先进方法。其在仅3个样本的情况下仍能准确识别干预目标（现有方法退化为随机猜测），同时稳健恢复共享因果图，证明了在数据稀缺场景下的有效性。
原文摘要 · Abstract (English)
Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions. We introduce MetaCaDI, the first framework to cast the identification of unknown interventions as a meta-learning problem, explicitly leveraging a jointly learned causal graph. MetaCaDI is a Bayesian framework that learns a shared causal structure across multiple environments and is optimized to rapidly adapt to new, few-shot intervention target identification tasks. A key innovation is our model's analytical adaptation, which uses a closed-form solution to bypass expensive and potentially unstable gradient-based bilevel optimization. Extensive experiments on synthetic and complex gene expression data demonstrate that MetaCaDI significantly outperforms state-of-the-art methods. It excels at identifying intervention targets from as few as 3 samples - where existing methods collapse to random chance - while robustly recovering the shared causal graph, proving its effectiveness in data-scarce scenarios.
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