Symmetric observations without symmetric causal explanations
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Abstract (arXiv)
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.
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References · 12
- 2021Convex optimizationcited by 2,479
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- 2023A Survey on Causal Discovery: Theory and Practicecited by 63
- 2013Learning Gaussian Networkscited by 62
- 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
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- 2021A convergent inflation hierarchy for quantum causal structurescited by 2
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- 2023Symmetric multipartite Bell inequalities via Frank-Wolfe algorithmscited by 1
- 2024The polarization hierarchy for polynomial optimization over convex bodies, with applications to nonnegative matrix rankcited by 1
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