The Relativity of Causal Knowledge
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913867219599360 |
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| author | D'Acunto, Gabriele Battiloro, Claudio |
| author_facet | D'Acunto, Gabriele Battiloro, Claudio |
| contents | Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning. Drawing inspiration from the revolution of Grothendieck in mathematics, we introduce the relativity of causal knowledge, which posits structural causal models (SCMs) are inherently imperfect, subjective representations embedded within networks of relationships. By leveraging category theory, we arrange SCMs into a functor category and show that their observational and interventional probability measures naturally form convex structures. This result allows us to encode non-intervened SCMs with convex spaces of probability measures. Next, using sheaf theory, we construct the network sheaf and cosheaf of causal knowledge. These structures enable the transfer of causal knowledge across the network while incorporating interventional consistency and the perspective of the subjects, ultimately leading to the formal, mathematical definition of relative causal knowledge. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_11718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Relativity of Causal Knowledge D'Acunto, Gabriele Battiloro, Claudio Artificial Intelligence Machine Learning Category Theory Methodology Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning. Drawing inspiration from the revolution of Grothendieck in mathematics, we introduce the relativity of causal knowledge, which posits structural causal models (SCMs) are inherently imperfect, subjective representations embedded within networks of relationships. By leveraging category theory, we arrange SCMs into a functor category and show that their observational and interventional probability measures naturally form convex structures. This result allows us to encode non-intervened SCMs with convex spaces of probability measures. Next, using sheaf theory, we construct the network sheaf and cosheaf of causal knowledge. These structures enable the transfer of causal knowledge across the network while incorporating interventional consistency and the perspective of the subjects, ultimately leading to the formal, mathematical definition of relative causal knowledge. |
| title | The Relativity of Causal Knowledge |
| topic | Artificial Intelligence Machine Learning Category Theory Methodology |
| url | https://arxiv.org/abs/2503.11718 |