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Related papers: Generalised Reichenbachian Common Cause Systems

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The concept of Probability of Causation (PC) is critically important in legal contexts and can help in many other domains. While it has been around since 1986, current operationalizations can obtain only the minimum and maximum values of…

Methodology · Statistics 2018-08-14 Tapajit Dey , Audris Mockus

Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a tool to visually represent causal variations between two…

Artificial Intelligence · Computer Science 2025-02-18 Charles K. Assaad

We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited model flexibility or poor model explainability and thus fails to…

Machine Learning · Computer Science 2020-02-20 Wei Zhang , Thomas Kobber Panum , Somesh Jha , Prasad Chalasani , David Page

Abstract Contextuality is a property of systems of random variables. The identity of a random variable in a system is determined by its joint distribution with all other random variables in the same context. When context changes, a variable…

Quantum Physics · Physics 2021-11-23 Ehtibar Dzhafarov

The concept of random dynamical system is a comparatively recent development combining ideas and methods from the well developed areas of probability theory and dynamical systems. Due to our inaccurate knowledge of the particular physical…

Dynamical Systems · Mathematics 2007-05-23 Vitor Araujo

With reference to a previous work, the problem of the experimental detection of non-causal synordination patterns between two series of physical events is examined. It is necessary that the patterns in question act in a reproducible, or at…

General Physics · Physics 2008-07-22 Leonardo Chiatti

Emergence is a pregnant property in various fields. It is the fact for a phenomenon to appear surprisingly and to be such that it seems at first sight that it is not possible to predict its apparition. That is the reason why it has often…

Computational Complexity · Computer Science 2023-08-23 Hervé Zwirn

The central limit theorem provides the theoretical foundation for the universality of the normal distribution: under broad conditions, the asymptotic distribution of a sum of independent random variables approaches a Gaussian. Yet, physical…

Data Analysis, Statistics and Probability · Physics 2026-03-26 Mario Castro , José A. Cuesta

Causality never gained the status of a "law" or "principle" in physics. Some recent literature even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged…

Quantum Physics · Physics 2019-09-19 Giacomo Mauro D'Ariano

We argue that (1) our perception of time through change and (2) the gap between reality and our observation of it are at the heart of both quantum mechanics and the dynamical mechanism of physical systems. We suggest that the origin of…

General Physics · Physics 2014-07-15 Michael York

Reduction is shown to be a possible consequence of the basic principles of quantum mechanics, involving no branching of the quantum state of the universe. The key feature of a measurement is attributed to the creation of macroscopic germs…

Quantum Physics · Physics 2008-02-19 Roland Omnès

Contextuality is a central property in comparative analysis of classical, quantum, and supercorrelated systems. We examine and compare two well-motivated approaches to contextuality. One approach ("contextuality-by-default") is based on the…

Quantum Physics · Physics 2016-01-21 J. Acacio de Barros , Ehtibar N. Dzhafarov , Janne V. Kujala , Gary Oas

We distinguish two sub-types of each of the two causality principles formulated in connection with the Common Cause Principle in [Hen05] and raise and investigate the problem of logical relations among the resulting four causality…

Quantum Physics · Physics 2012-04-20 Miklós Rédei , Iñaki San Pedro

The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been…

Machine Learning · Statistics 2019-10-22 Finnian Lattimore , David Rohde

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information…

Econometrics · Economics 2024-05-20 Silvia Goncalves , Serena Ng

Causal quantum theory assumes that measurements or collapses are well-defined physical processes, localised in space-time, and never give perfectly reliable outcomes and that the outcome of one measurement only influences the outcomes of…

Quantum Physics · Physics 2021-11-09 Adrian Kent

The most promising candidate for being the fundamental principle of quantum contextuality is the Exclusivity principle, which states that the sum of the probabilities of a set of pairwise exclusive events cannot exceed 1. By itself, the…

Quantum Physics · Physics 2016-10-31 Barbara Amaral

Quantum theory is a mathematical formalism to compute probabilities for outcomes happenning in physical experiments. These outcomes constitute events happening in space-time. One of these events represents the fact that a system located in…

Quantum Physics · Physics 2012-06-07 Marco Zaopo

The uncertainty principle lies at the heart of quantum physics, and is widely thought of as a fundamental limit on the measurement precisions of incompatible observables. Here we show that the traditional uncertainty relation in fact…

Quantum Physics · Physics 2021-02-03 Jun-Li Li , Cong-Feng Qiao

Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive…

Machine Learning · Computer Science 2019-02-15 Pei Wang , Pushpi Paranamana , Patrick Shafto
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