Uncovering the underlying 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 joint discovery of a causal graph and unknown interventions as a meta-learning problem. MetaCaDI is a Bayesian framework that learns a shared causal graph structure across multiple experiments and is optimized to rapidly adapt to new, few-shot intervention target prediction 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 both causal graph recovery and identifying intervention targets from as few as 10 data instances, proving its robustness in data-scarce scenarios.

翻译：揭示复杂现实世界系统的潜在因果机制仍然是一项重大挑战，因为这些系统通常涉及高昂的数据收集成本以及未知的干预。我们提出了MetaCaDI，这是首个将因果图与未知干预的联合发现构建为元学习问题的框架。MetaCaDI是一个贝叶斯框架，它能够学习跨多个实验的共享因果图结构，并经过优化以快速适应新的、小样本干预目标预测任务。一个关键创新在于我们模型的解析适应机制，它采用闭式解来绕过昂贵且可能不稳定的基于梯度的双层优化。在合成数据和复杂基因表达数据上进行的大量实验表明，MetaCaDI显著优于现有最先进方法。该框架在因果图恢复和从仅10个数据实例中识别干预目标方面均表现出色，证明了其在数据稀缺场景下的鲁棒性。