Bounding Freedom of Choice via Causal Network Embeddings

arXiv:2611.14791v1 Announce Type: new Abstract: Standard Bell inequalities rely on measurement independence, the assumption that measurement settings are perfectly decoupled from the source of correlations. We demonstrate that embedding Bell scenarios into larger causal networks naturally overcomes this limitation. This embedding yields experimentally accessible lower and upper bounds on measurement dependence, the incompatibility of which certifies nonclassicality directly from data, without assuming a priori freedom of choice. We formulate a general linear-programming framework to systematically derive these measurement-dependence consistency (MDC) Bell inequalities.