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Related papers: Bell's local causality is a d-separation criterion

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Seen from the modern lens of causal inference, Bell's theorem is nothing else than the proof that a specific classical causal model cannot explain quantum correlations. It is thus natural to move beyond Bell's paradigmatic scenario and…

Franson showed that Aspect's experiment to test Bell's inequality did not rule out local realistic theories with delayed determinism. A class of local, deterministic discrete mathematical models with delayed determinism is described that…

High Energy Physics - Theory · Physics 2008-02-03 Paul P. Budnik

Recently, the quantumness of local correlations arising from separable states in the context of a Bell scenario has been studied and linked with superlocality [Phys. Rev. A {\bf 95}, 032120 (2017)]. Here we investigate the quantumness of…

In his late piece 'La nouvelle cuisine' (Bell 1990), John Bell describes the steps from an intuitive, informal principle of locality to a mathematical rule called Factorizability. This rule stipulates that when possible past causes are held…

Quantum Physics · Physics 2026-02-20 Huw Price

In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world…

Machine Learning · Computer Science 2018-12-14 Chengsheng Mao , Lijuan Lu , Bin Hu

Relying on some auxiliary assumptions, usually considered mild, Bell's theorem proves that no local theory can reproduce all the predictions of quantum mechanics. In this work, we introduce a fully local, superdeterministic model that, by…

Quantum Physics · Physics 2020-08-04 G. S. Ciepielewski , E. Okon , D. Sudarsky

We derive a set of Bell-type inequalities for arbitrarily high-dimensional systems, based on the assumption of partial separability in the hybrid local-nonlocal hidden variable model. Partially entangled states would not violate the…

Quantum Physics · Physics 2015-05-20 Jing-Ling Chen , Dong-Ling Deng , Hong-Yi Su , Chunfeng Wu , C. H. Oh

Causal Bayesian networks are 'causal' models since they make predictions about interventional distributions. To connect such causal model predictions to real-world outcomes, we must determine which actions in the world correspond to which…

Machine Learning · Statistics 2025-02-04 Frederik Hytting Jørgensen , Luigi Gresele , Sebastian Weichwald

A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We…

Artificial Intelligence · Computer Science 2013-01-30 Julian R. Neil , Chris S. Wallace , Kevin B. Korb

We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal…

Machine Learning · Statistics 2016-11-07 Krzysztof Chalupka , Frederick Eberhardt , Pietro Perona

The entanglement and the violation of Bell and CHSH inequalities in spin polarization correlation experiments (SPCE) is considered to be one of the biggest mysteries of Nature and is called quantum nonlocality. In this paper we show once…

Quantum Physics · Physics 2014-10-29 Marian Kupczynski

In this chapter we discuss the Einstein Podolsky Rosen theorem and its strong relation with Bell's theorem. The central role played by the concept of beable introduced by Bell is emphasized. In particular we stress that beables involved in…

Quantum Physics · Physics 2023-11-10 Aurélien Drezet

We examine the locality assumption of Bell's theorem in three steps of EPRB experiment. Depending on the context, locality is embodied in the conditions of separability, local causality, factorizability, relativistic causality, and…

Quantum Physics · Physics 2017-04-28 Afshin Shafiee , Farhad Taher Ghahramani

Causal discovery is crucial for causal inference in observational studies, as it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global causal discovery is notoriously hard in the…

Machine Learning · Statistics 2024-06-04 Jacqueline Maasch , Weishen Pan , Shantanu Gupta , Volodymyr Kuleshov , Kyra Gan , Fei Wang

Linear causal disentanglement is a recent method in causal representation learning to describe a collection of observed variables via latent variables with causal dependencies between them. It can be viewed as a generalization of both…

Machine Learning · Statistics 2024-07-08 Paula Leyes Carreno , Chiara Meroni , Anna Seigal

The precision with which we can measure operators that do not commute with conserved quantities is limited by the need to preserve the associated global symmetries. We show how to construct a local hidden-variable model that violates Bell…

Quantum Physics · Physics 2025-11-11 Alejandro J. Garza , Jonte R. Hance

J.S. Bell's work has convinced many that correlations in violation of CHSH inequalities show that the world itself is non-local, and that there is an apparently essential conflict between any sharp formulation of quantum theory and…

Quantum Physics · Physics 2012-08-01 Richard Healey

We provide a unified operational framework for the study of causality, non-locality and contextuality, in a fully device-independent and theory-independent setting. We define causaltopes, our chosen portmanteau of "causal polytopes", for…

Quantum Physics · Physics 2023-07-31 Stefano Gogioso , Nicola Pinzani

It is well-known that if one assumes quantum theory to hold locally, then processes with indefinite causal order and cyclic causal structures become feasible. Here, we study qualitative limitations on causal structures and correlations…

Quantum Physics · Physics 2024-01-09 Eleftherios-Ermis Tselentis , Ämin Baumeler

We consider causal models with two observed variables and one latent variables, each variable being discrete, with the goal of characterizing the possible distributions on outcomes that can result from controlling one of the observed…

Information Theory · Computer Science 2021-03-05 Kevin Shu