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Drawing from the theory of optimal transport we propose a rigorous notion of a causal relation for Borel probability measures on a given spacetime. To prepare the ground, we explore the borderland between causality, topology and measure…

Mathematical Physics · Physics 2020-12-21 Michał Eckstein , Tomasz Miller

We perform an in-depth analysis of the transformation rules under duality for couplings of theories containing multiple scalars, $p$-form gauge fields, linearized gravitons or $(p,1)$ mixed symmetry tensors. Following a similar reasoning to…

High Energy Physics - Theory · Physics 2021-04-07 Athanasios Chatzistavrakidis , Georgios Karagiannis , Arash Ranjbar

It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems. Existing learning approaches rely on establishing…

Machine Learning · Computer Science 2022-02-18 Mengyue Yang , Xinyu Cai , Furui Liu , Xu Chen , Zhitang Chen , Jianye Hao , Jun Wang

A unifying framework for the study of causal relations is presented. The causal relations are regarded as subsets of M x M and the role of the corresponding antisymmetry conditions in the construction of the causal ladder is stressed. The…

General Relativity and Quantum Cosmology · Physics 2011-07-08 E. Minguzzi

We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if both the covariance matrix of the cause and the…

Machine Learning · Statistics 2009-09-25 Dominik Janzing , Patrik O. Hoyer , Bernhard Schoelkopf

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex…

Neurons and Cognition · Quantitative Biology 2021-12-30 X. San Liang

Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer…

Machine Learning · Computer Science 2022-11-09 Rezaur Rashid , Jawad Chowdhury , Gabriel Terejanu

The discovery of colour-kinematic duality has led to significant progress in the computation of scattering amplitudes in quantum field theories. At tree level, the origin of the duality can be traced back to the monodromies of open-string…

High Energy Physics - Theory · Physics 2017-10-17 Alexander Ochirov , Piotr Tourkine , Pierre Vanhove

The dual normal factor graph and the factor graph duality theorem have been considered for discrete graphical models. In this paper, we show an application of the factor graph duality theorem to continuous graphical models. Specifically, we…

Methodology · Statistics 2021-11-04 Mehdi Molkaraie

An overview of a quantum algorithm application for the identification of causal singular configurations of multiloop Feynman diagrams is presented. The quantum algorithm is implemented in two different quantum simulators, the output…

High Energy Physics - Phenomenology · Physics 2022-01-13 Selomit Ramírez-Uribe

The treewidth boundedness problem for a logic asks for the existence of an upper bound on the treewidth of the models of a given formula in that logic. This problem is found to be undecidable for first order logic. We consider a…

Logic in Computer Science · Computer Science 2024-05-20 Marius Bozga , Lucas Bueri , Radu Iosif , Florian Zuleger

Accessibility percolation is a new type of percolation problem inspired by evolutionary biology. To each vertex of a graph a random number is assigned and a path through the graph is called accessible if all numbers along the path are in…

Statistical Mechanics · Physics 2013-04-04 Stefan Nowak , Joachim Krug

Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only…

Statistics Theory · Mathematics 2008-04-24 Dominik Janzing , Bernhard Schoelkopf

This paper presents a method to identify causal interactions between two time series. The largest eigenvalue follows a Tracy-Widom distribution, derived from a Coulomb gas model. This defines causal interactions as the pushing and pulling…

Portfolio Management · Quantitative Finance 2025-11-19 Alejandro Rodriguez Dominguez , Om Hari Yadav

The hypothesis that the causal properties of space-time, as well as other properties of physical systems like unitarity, charge conservation, etc., might be decided by the higher dimensional structure (in particular, higher-dimensional…

High Energy Physics - Phenomenology · Physics 2007-06-26 Israel Quiros

Understanding and predicting how complex systems respond to external perturbations is a central challenge in nonequilibrium statistical physics. Here we consider continuous-time Markov networks, which we subject to perturbations along a…

Statistical Mechanics · Physics 2026-02-25 Robin Bebon , Thomas Speck

Causality in quantum field theory is defined by the vanishing of field commutators for space-like separations. However, this does not imply a direction for causal effects. Hidden in our conventions for quantization is a connection to the…

High Energy Physics - Theory · Physics 2019-10-30 John F. Donoghue , Gabriel Menezes

Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to…

High Energy Physics - Theory · Physics 2018-03-13 Rafael D. Sorkin , Yasaman K. Yazdi

We study a class of generalized Abelian gauge field theories where CPT symmetry is violated by a Chern-Simons-like term which selects a preferred direction in spacetime. Such Chern-Simons-like terms may either emerge as part of the…

High Energy Physics - Phenomenology · Physics 2009-11-07 C. Adam , F. R. Klinkhamer

We present a categorical framework for relating causal models that represent the same system at different levels of abstraction. We define a causal abstraction as natural transformations between appropriate Markov functors, which concisely…

Machine Learning · Statistics 2025-10-07 Markus Englberger , Devendra Singh Dhami
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