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Superdeterminism - where the Measurement Independence assumption in Bell's Theorem is violated - is frequently assumed to imply implausibly conspiratorial correlations between properties $\lambda$ of particles being measured and measurement…

Quantum Physics · Physics 2024-01-09 Tim Palmer

Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, however, can be unreliable, and algorithms are sensitive to…

Machine Learning · Computer Science 2026-04-21 Philipp M. Faller , Dominik Janzing

Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision…

Computer Science and Game Theory · Computer Science 2023-06-22 Philippe Mongin

A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional…

Artificial Intelligence · Computer Science 2014-11-17 N. L. Zhang , D. Poole

The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domains. Methods extracting causal information from conditional…

Machine Learning · Statistics 2020-10-13 Daniel Chicharro , Michel Besserve , Stefano Panzeri

Constraint-based causal discovery algorithms utilize many statistical tests for conditional independence to uncover networks of causal dependencies. These approaches to causal discovery rely on an assumed correspondence between the…

Machine Learning · Computer Science 2025-04-18 Bijan Mazaheri , Jiaqi Zhang , Caroline Uhler

We argue that for the proof of Bell's theorem no assumptions about realism or free will are necessary. The key formula \[E(AB|a,b) = \int A(a,b,\lambda)B(a,b,\lambda)\rho(\lambda) d\lambda\] follows from the logic of plausible reasoning…

General Physics · Physics 2018-05-04 I. Schmelzer

Quantum mechanics provides a statistical description about nature, and thus would be incomplete if its statistical predictions could not be accounted for some realistic models with hidden variables. There are, however, two powerful theorems…

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

One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks with hidden variables give rise to highly non-trivial…

Machine Learning · Statistics 2014-10-14 R. Chaves , L. Luft , T. O. Maciel , D. Gross , D. Janzing , B. Schölkopf

Nonlocality, as demonstrated by the violation of Bell inequalities, enables device-independent cryptographic tasks that do not require users to trust their apparatus. In this article, we consider devices whose inputs are spatiotemporal…

Quantum Physics · Physics 2020-02-12 Andrew J. P. Garner , Marius Krumm , Markus P. Mueller

A classical system violating the Bell inequality is discussed. The system is local, deterministic, observers have free will, and detectors are ideal so that no data are lost. The trick is based on two elements. First, a state of one…

Quantum Physics · Physics 2009-09-25 Marek Czachor

Causal networks beyond that in the paradigmatic Bell's theorem can lead to new kinds and applications of non-classical behavior. Their study, however, has been hindered by the fact that they define a non-convex set of correlations and only…

Quantum Physics · Physics 2023-11-16 Giulio Camillo , Pedro Lauand , Davide Poderini , Rafael Rabelo , Rafael Chaves

The contextuality of quantum mechanics, i.e. the measurement outcome dependence upon previously made measurements, can be shown by the violation of inequalities based on measurements of well chosen observables. An important property of such…

Quantum Physics · Physics 2017-03-16 A. Laversanne-Finot , A. Ketterer , M. R. Barros , S. P. Walborn , T. Coudreau , A. Keller , P. Milman

Since Bell's theorem, it is known that the concept of local realism fails to explain quantum phenomena. Indeed, the violation of a Bell inequality has become a synonym of the incompatibility of quantum theory with our classical notion of…

Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the…

Machine Learning · Computer Science 2021-02-08 Samir Wadhwa , Roy Dong

We show that the ability to consider counterfactual situations is a necessary assumption of Bell's theorem, and that, to allow Bell inequality violations while maintaining all other assumptions, we just require certain measurement choices…

Quantum Physics · Physics 2024-12-25 Jonte R. Hance

We define criteria for a hidden variables theory to be Lorentz invariant and prove that it implies no signaling. As a result, we show that a Lorentz invariant and contextual theory (e.g., quantum field theory) must be genuinely stochastic,…

Quantum Physics · Physics 2025-03-18 Avi Levy , Meir Hemmo

The validity OF a causal model can be tested ONLY IF the model imposes constraints ON the probability distribution that governs the generated data. IN the presence OF unmeasured variables, causal models may impose two types OF constraints :…

Artificial Intelligence · Computer Science 2013-01-07 Jin Tian , Judea Pearl

Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized…

Machine Learning · Statistics 2025-03-19 Boyang Sun , Yu Yao , Guang-Yuan Hao , Yumou Qiu , Kun Zhang