Symmetric observations without symmetric causal explanations
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910077193027584 |
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| author | William, Christian Remy, Patrick Bancal, Jean-Daniel Cai, Yu Brunner, Nicolas Pozas-Kerstjens, Alejandro |
| author_facet | William, Christian Remy, Patrick Bancal, Jean-Daniel Cai, Yu Brunner, Nicolas Pozas-Kerstjens, Alejandro |
| contents | Inferring causal models from observed correlations is a challenging task, crucial to many areas of science. In order to alleviate the computational effort when sifting through possible causal explanations for some given observations, it is important to know whether symmetries in the observations correspond to symmetries in the underlying realization so that one can quickly discard impossible explanations. Via an explicit example, we demonstrate that, in general, symmetries cannot be exploited to reduce the hypothesis space. We use a tripartite probability distribution over binary events that is realized by using three (different) independent sources of classical randomness. We prove that even removing the condition that the sources distribute systems described by classical physics, the requirements that (i) the sources distribute the same physical systems, (ii) these physical systems respect relativistic causality, and (iii) the correlations are the observed ones are incompatible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symmetric observations without symmetric causal explanations William, Christian Remy, Patrick Bancal, Jean-Daniel Cai, Yu Brunner, Nicolas Pozas-Kerstjens, Alejandro Quantum Physics Machine Learning Statistics Theory Inferring causal models from observed correlations is a challenging task, crucial to many areas of science. In order to alleviate the computational effort when sifting through possible causal explanations for some given observations, it is important to know whether symmetries in the observations correspond to symmetries in the underlying realization so that one can quickly discard impossible explanations. Via an explicit example, we demonstrate that, in general, symmetries cannot be exploited to reduce the hypothesis space. We use a tripartite probability distribution over binary events that is realized by using three (different) independent sources of classical randomness. We prove that even removing the condition that the sources distribute systems described by classical physics, the requirements that (i) the sources distribute the same physical systems, (ii) these physical systems respect relativistic causality, and (iii) the correlations are the observed ones are incompatible. |
| title | Symmetric observations without symmetric causal explanations |
| topic | Quantum Physics Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2502.14950 |