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In this thesis we investigate the importance of causality in non-perturbative approaches to quantum gravity. Firstly, causal sets are introduced as a simple kinematical model for causal geometry. It is shown how causal sets could account…

High Energy Physics - Theory · Physics 2016-09-08 Stefan Zohren

We combine the "evolving constants" approach to the construction of observables in canonical quantum gravity with the Page--Wootters formulation of quantum mechanics with a relational time for generally covariant systems. This overcomes the…

General Relativity and Quantum Cosmology · Physics 2009-11-06 Rodolfo Gambini , Rafael Porto , Sebastian Torterolo , Jorge Pullin

Identifying causal order from restricted projective data is generally nontrivial. When two quantum players interact only through an unobserved environment, the available local measurement statistics are typically not tomographically…

Quantum Physics · Physics 2026-05-07 Masahito Hayashi

Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. By mapping probability measures into a reproducing kernel Hilbert space (RKHS), kernel embeddings enable…

Machine Learning · Statistics 2024-10-31 Dino Sejdinovic

Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting the patterns of objective systems. This phenomenon is…

Information Theory · Computer Science 2024-03-08 Liye Jia , Fengyufan Yang , Ka Lok Man , Erick Purwanto , Sheng-Uei Guan , Jeremy Smith , Yutao Yue

In ordinary, non-relativistic, quantum physics, time enters only as a parameter and not as an observable: a state of a physical system is specified at a given time and then evolved according to the prescribed dynamics. While the state can,…

Quantum Physics · Physics 2013-02-13 Joseph Fitzsimons , Jonathan Jones , Vlatko Vedral

In many causal inference problems, multiple action variables, such as factors, mediators, or network units, often share a common causal role yet lack a natural ordering. To avoid ambiguity, the scientific interpretation of a vector of…

Methodology · Statistics 2026-01-26 Jiaqi Tong , Fan Li

The statistics of local measurements of joint quantum systems can sometimes be used to distinguish the spatiotemporal structure in which they were measured. We first prove that every bipartite separable density matrix is temporally…

Quantum Physics · Physics 2026-01-05 Minjeong Song , Arthur J. Parzygnat

Following a review of quantum-classical hybrid dynamics, we discuss the ensuing proliferation of observables and relate it to measurements of (would-be) quantum mechanical degrees of freedom performed by (would-be) classical ones (if they…

Quantum Physics · Physics 2013-03-06 Hans-Thomas Elze

John Bell once argued that one ought to select, out of the 'observables' of quantum theory, some subset of 'beables' that can be consistently ascribed determinate values. Moreover, this subset should be selected so as to guarantee (among…

Quantum Physics · Physics 2007-05-23 Rob Clifton

We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those…

Statistics Theory · Mathematics 2024-11-08 Natalie Neumeyer , Leonie Selk

The problem of defining quantum probabilities of composite events is considered. This problem is of high importance for the theory of quantum measurements and for quantum decision theory that is a part of measurement theory. We show that…

Quantum Physics · Physics 2015-06-17 V. I. Yukalov , D. Sornette

Mathematical models of the real world are simplified representations of complex systems. A caveat to using mathematical models is that predicted causal effects and conditional independences may not be robust under model extensions, limiting…

Methodology · Statistics 2022-08-30 Tineke Blom , Joris M. Mooij

In the Contextuality-by-Default theory random variables representing measurement outcomes are labeled contextually, i.e., not only by what they measure but also under what conditions (in what contexts) the measurements are made, including…

Quantum Physics · Physics 2018-12-11 Ehtibar N. Dzhafarov

Bell inequalities follow from a set of seemingly natural assumptions about how to provide a causal model of a Bell experiment. In the face of their violation, two types of causal models that modify some of these assumptions have been…

Quantum Physics · Physics 2022-05-11 Patrick J. Daley , Kevin J. Resch , Robert W. Spekkens

A powerful tool for the analysis of nonrandomized observational studies has been the potential outcomes model. Utilization of this framework allows analysts to estimate average treatment effects. This article considers the situation in…

Statistics Theory · Mathematics 2019-05-31 Debashis Ghosh , Efrén Cruz-Cortés

The notion of causal effect is fundamental across many scientific disciplines. Traditionally, quantitative researchers have studied causal effects at the level of variables; for example, how a certain drug dose (W) causally affects a…

Methodology · Statistics 2026-04-07 Junhyung Park , Yuqing Zhou

In this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is natural for economic time series but remains…

Statistics Theory · Mathematics 2018-12-31 Jozef Baruník , Tobias Kley

We present a detailed motivation for and definition of the contextual values of an observable, which were introduced by Dressel et al. [Phys. Rev. Lett. 104 240401 (2010)]. The theory of contextual values extends the well-established theory…

Quantum Physics · Physics 2012-02-29 J. Dressel , A. N. Jordan

With the widespread application of causal inference, it is increasingly important to have tools which can test for the presence of causal effects in a diverse array of circumstances. In this vein we focus on the problem of testing for…

Machine Learning · Statistics 2023-11-08 Jake Fawkes , Robert Hu , Robin J. Evans , Dino Sejdinovic