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Related papers: Causal structures and causal boundaries

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In fundamental theories that accounts for quantum gravitational effects, the spacetime causal structure is expected to be quantum uncertain. Previous studies of quantum causal structure focused on finite-dimensional systems. Here we present…

Quantum Physics · Physics 2019-02-18 Ding Jia

Casual structure can take the form of cone bundles on a manifold, more general local preorders on a topological space, or simplicial orientations implicit in a simplicial set. This note takes a triangulation of a conal manifold M to mean an…

General Topology · Mathematics 2019-09-05 Sanjeevi Krishnan

The notion of causal boundary $\partial M$ for a strongly causal spacetime $M$ has been a controversial topic along last decades: on one hand, some attempted definitions were not fully consistent, on the other, there were simple examples…

Mathematical Physics · Physics 2012-10-17 J. L. Flores , J. Herrera , M. Sanchez

In general relativity, `causal structure' refers to the partial order on space-time points (or regions) that encodes time-like relationships. Recently, quantum information and quantum foundations saw the emergence of a `causality…

Quantum Physics · Physics 2017-08-16 Aleks Kissinger , Matty Hoban , Bob Coecke

Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and…

Machine Learning · Computer Science 2010-07-16 Kamran Karimi

Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for…

Machine Learning · Computer Science 2022-12-12 Kai Lagemann , Christian Lagemann , Bernd Taschler , Sach Mukherjee

Linear structural equation models represent direct causal effects as directed edges and confounding factors as bidirected edges. An open problem is to identify the causal parameters from correlations between the nodes. We investigate…

Artificial Intelligence · Computer Science 2022-03-07 Benito van der Zander , Marcel Wienöbst , Markus Bläser , Maciej Liśkiewicz

Causal models capture cause-effect relations both qualitatively - via the graphical causal structure - and quantitatively - via the model parameters. They offer a powerful framework for analyzing and constructing processes. Here, we…

Quantum Physics · Physics 2025-12-02 Ämin Baumeler , Stefan Wolf

We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots,X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG). To this end, we first…

Machine Learning · Statistics 2019-12-06 Dominik Janzing , Kailash Budhathoki , Lenon Minorics , Patrick Blöbaum

A change of spatial topology in a causal, compact spacetime cannot occur when the metric is globally Lorentzian. One can however construct a causal metric from a Riemannian metric and a Morse function on the background cobordism manifold,…

General Relativity and Quantum Cosmology · Physics 2009-10-31 A. Borde , H. F. Dowker , R. S. Garcia , R. D. Sorkin , S. Surya

We derive all possible causality conditions for conformally flat Lorentzian metrics on the two-dimensional cylinder.

Differential Geometry · Mathematics 2011-04-01 Alexander Dirmeier

I present aspects of causal set theory (a research programme in quantum gravity) as being en route to achieving a reduction of Lorentzian geometry to causal sets. I take reduction in philosophers' sense; and I argue that the prospects are…

History and Philosophy of Physics · Physics 2024-01-30 Jeremy Butterfield

Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information to make the range of values as narrow as possible, are…

Methodology · Statistics 2025-03-26 Gustav Jonzon , Erin E Gabriel , Arvid Sjölander , Michael C Sachs

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention ("effects of causes"), and studying whether there is a causal link between the observed exposure and outcome in an…

Statistics Theory · Mathematics 2021-04-02 A. Philip Dawid , Monica Musio

Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of `fairness' in comparisons dates back several hundred years,…

Other Statistics · Statistics 2022-04-06 Erica EM Moodie , David A Stephens

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent…

Machine Learning · Statistics 2022-08-31 Patrick Forré , Joris M. Mooij

The aim of this paper is two-fold. First, the study of $C_{12}$-structure (called by us corner structure) is extended to the general case without any condition, unlike our previous papers (see, \cite{BB, BG2, BG, BBB}). Second, starting…

Differential Geometry · Mathematics 2023-07-31 Beldjilali Gherici

A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal…

Artificial Intelligence · Computer Science 2020-08-04 Marco Zaffalon , Alessandro Antonucci , Rafael Cabañas

We present several problems and results relating the scalar curvatures of manifolds with mean curvatures of their boundaries

Differential Geometry · Mathematics 2019-02-12 Misha Gromov

Structural causal models (SCMs) are a widespread formalism to deal with causal systems. A recent direction of research has considered the problem of relating formally SCMs at different levels of abstraction, by defining maps between SCMs…

Artificial Intelligence · Computer Science 2022-07-19 Fabio Massimo Zennaro
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