English
Related papers

Related papers: Detecting Causality with Conjugation Quandles over…

200 papers

A quandle coloring quiver is a quiver structure, introduced by Karina Cho and Sam Nelson, which is defined on the set of quandle colorings of an oriented knot or link by a finite quandle. We study quandle coloring quivers of (p, 2)-torus…

Geometric Topology · Mathematics 2022-09-13 Jagdeep Basi , Carmen Caprau

We incorporate quandle cocycle information into the quandle coloring quivers we defined in arXiv:1807.10465 to define weighted directed graph-valued invariants of oriented links we call \textit{quandle cocycle quivers}. This construction…

Geometric Topology · Mathematics 2019-04-22 Karina Cho , Sam Nelson

We describe the package "IncidenceCorrespondenceCohomology" for the computer algebra system Macaulay2. The main feature concerns the computation of characters and dimensions for the cohomology groups of line bundles on the incidence…

Algebraic Geometry · Mathematics 2025-03-25 Annet Kyomuhangi , Emanuela Marangone , Claudiu Raicu , Ethan Reed

This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges…

Methodology · Statistics 2023-10-26 Minjie Wang , Xiaotong Shen , Wei Pan

Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality…

Data Analysis, Statistics and Probability · Physics 2016-05-20 Massimiliano Zanin

Confounding seriously impairs our ability to learn about causal relations from observational data. Confounding can be defined as a statistical association between two variables due to inputs from a common source (the confounder). For…

Methodology · Statistics 2018-05-17 Anders Ledberg

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a…

Methodology · Statistics 2024-05-07 Vittorio Del Tatto , Gianfranco Fortunato , Domenica Bueti , Alessandro Laio

We consider a quiver structure on the set of quandle colorings of an oriented knot or link diagram. This structure contains a wealth of knot and link invariants and provides a categorification of the quandle counting invariant in the most…

Geometric Topology · Mathematics 2018-10-09 Karina Cho , Sam Nelson

If $L$ is a classical link then the multivariate Alexander quandle, $Q_A(L)$, is a substructure of the multivariate Alexander module, $M_A(L)$. In the first paper of this series we showed that if two links $L$ and $L'$ have $Q_A(L) \cong…

Geometric Topology · Mathematics 2019-11-13 Lorenzo Traldi

Two wave fronts $W_1$ and $W_2$ that originated at some points of the manifold $M^n$ are said to be causally related if one of them passed through the origin of the other before the other appeared. We define the causality relation invariant…

Geometric Topology · Mathematics 2007-05-23 Vladimir Chernov , Yuli B. Rudyak

In this paper, we study the colorability of link diagrams by the Alexander quandles. We show that if the reduced Alexander polynomial $\Delta_{L}(t)$ is vanishing, then $L$ admits a non-trivial coloring by any non-trivial Alexander quandle…

Geometric Topology · Mathematics 2011-05-19 Yongju Bae

Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not guaranteed to be correct. Expert recommendations may contradict data-driven results, and their…

We completely characterize the coloring quivers of general torus links by dihedral quandles by first exhausting all possible numbers of colorings, followed by determining the interconnections between colorings in each case. The quiver is…

Geometric Topology · Mathematics 2024-03-08 Mohamed Elhamdadi , Brooke Jones , Minghui Liu

Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of…

Data Analysis, Statistics and Probability · Physics 2025-08-05 Álvaro Martínez-Sánchez , Adrián Lozano-Durán

The classical causal relations between a set of variables, some observed and some latent, can induce both equality constraints (typically conditional independences) as well as inequality constraints (Instrumental and Bell inequalities being…

Quantum Physics · Physics 2024-04-11 Shashaank Khanna , Marina Maciel Ansanelli , Matthew F. Pusey , Elie Wolfe

We discuss multivariable invariants of colored links associated with the $N$-dimensional root of unity representation of the quantum group. The invariants for $N>2$ are generalizations of the multi-variable Alexander polynomial. The…

High Energy Physics - Theory · Physics 2008-02-03 Tetsuo Deguchi

Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discovery algorithms typically require parametric restrictions or…

Methodology · Statistics 2022-06-29 David S. Watson , Ricardo Silva

We give a complete description of the associated group of any quandle as a central extension of the inner-automorphism group. As an application, we compute the second quandle homology groups of quandles of some families, including those of…

Geometric Topology · Mathematics 2024-02-26 Katsumi Ishikawa

The knot coloring polynomial defined by Eisermann for a finite pointed group is generalized to an infinite pointed group as the longitudinal mapping invariant of a knot. In turn this can be thought of as a generalization of the quandle…

Geometric Topology · Mathematics 2018-02-27 W. Edwin Clark , Masahico Saito

If $X,Y,Z$ denote sets of random variables, two different data sources may contain samples from $P_{X,Y}$ and $P_{Y,Z}$, respectively. We argue that causal discovery can help inferring properties of the `unobserved joint distributions'…

Machine Learning · Statistics 2023-05-12 Dominik Janzing , Philipp M. Faller , Leena Chennuru Vankadara