MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917042438799360 |
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| author | Ong, Hans Jarett Chikahara, Yoichi Iwata, Tomoharu |
| author_facet | Ong, Hans Jarett Chikahara, Yoichi Iwata, Tomoharu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions Ong, Hans Jarett Chikahara, Yoichi Iwata, Tomoharu Machine Learning 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. |
| title | MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.22298 |