Causal Abstraction Networks: A Sheaf-Theoretic Framework for Distributed Causal AI
A new paper introduces the causal abstraction network (CAN), which is based on sheaf theory. This framework aims to bring together different and sometimes conflicting causal perspectives from agents who have limited access to their surroundings. It addresses a key challenge in causal AI that hasn’t received much attention, as most existing models depend on one widely accepted causal framework. CAN can learn, represent, and reason through a mix of causal models, incorporating various existing models that consider context. Sheaf theory provides a strong foundation for aligning distributed causal insights without needing explicit graphs or interventional data. The paper also describes a categorical approach to this framework.
Key facts
- The paper is arXiv:2509.25236v3.
- It introduces the causal abstraction network (CAN).
- CAN is a sheaf-theoretic framework.
- It coordinates multiple causal perspectives from distributed agents.
- Agents have limited and heterogeneous access to the environment.
- Existing frameworks assume a single shared global causal model.
- CAN works with mixture of causal models (MCMs).
- Sheaf theory enables alignment without explicit causal graphs or interventional data.
Entities
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