MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
arXiv:2510.22298v2 Announce Type: replace-cross Abstract: 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 identi
The increasing complexity of real-world AI applications, coupled with high data collection costs and the prevalence of unknown interventions, necessitates more robust causal discovery methods.
This framework offers a significant step towards more reliable and adaptable AI systems by improving the ability to understand and predict causality in environments with unobserved changes, which is crucial for advanced AI agents.
The ability to identify unknown interventions and rapidly adapt to new causal structures via meta-learning will accelerate AI development in dynamic real-world settings, reducing the need for extensive retraining.
- · AI agents developers
- · Robotics
- · Complex systems modeling
- · Reinforcement learning
- · Traditional causal inference methods
- · Systems reliant on static models
More robust and adaptable AI systems capable of operating in complex and dynamically changing environments.
Accelerated development and deployment of autonomous systems, including advanced AI agents, capable of handling unforeseen circumstances.
Reduced costs and increased efficiency in data-intensive AI development, potentially leading to faster commercialization and broader adoption of AI agents across industries.
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