English

Symmetric observations without symmetric causal explanations

Quantum Physics 2026-03-30 v2 Machine Learning Statistics Theory Machine Learning Statistics Theory

Abstract

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.

Keywords

Cite

@article{arxiv.2502.14950,
  title  = {Symmetric observations without symmetric causal explanations},
  author = {Christian William and Patrick Remy and Jean-Daniel Bancal and Yu Cai and Nicolas Brunner and Alejandro Pozas-Kerstjens},
  journal= {arXiv preprint arXiv:2502.14950},
  year   = {2026}
}

Comments

8+3 pages, 4+1 figures, RevTeX 4.2. The computational appendix is available at https://www.github.com/apozas/symmetric-causal. V2: published version

R2 v1 2026-06-28T21:51:58.307Z