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In the light of some recent results, it is argued that usual concepts of causality and locality are approximations valid at scales greater than the Compton wavelength and corresponding time scales. It follows that the "spooky" non-locality…

Quantum Physics · Physics 2007-05-23 B. G. Sidharth

Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever more complex, high-level tasks make systematic evaluation more…

Machine Learning · Computer Science 2025-02-10 Felix Leeb , Zhijing Jin , Bernhard Schölkopf

Classical causal models, such as Granger causality and structural equation modeling, are largely restricted to acyclic interactions and struggle to represent cyclic and higher-order dynamics in complex networks. We introduce a causal…

Neurons and Cognition · Quantitative Biology 2026-04-21 Moo K. Chung , D. Vijay Anand , Anass B El-Yaagoubi , Jae-Hun Jung , Anqi Qiu , Hernando Ombao

The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining optimal risk bounds for point estimates acquired from…

Statistics Theory · Mathematics 2017-12-27 Debdeep Pati , Anirban Bhattacharya , Yun Yang

Causal influences are at the core of any empirical science, the reason why its quantification is of paramount relevance for the mathematical theory of causality and applications. Quantum correlations, however, challenge our notion of cause…

Quantum Physics · Physics 2023-09-20 Lucas Hutter , Rafael Chaves , Ranieri Nery , George Moreno , Daniel J. Brod

We develop a new interpretation of quantum theory by combining insights from extended Wigner's friend scenarios and quantum causal modelling. In this interpretation, which synthesizes ideas from relational quantum mechanics and consistent…

Quantum Physics · Physics 2024-02-01 Nick Ormrod , Jonathan Barrett

We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects…

Artificial Intelligence · Computer Science 2012-07-02 Changsung Kang , Jin Tian

Eugene Wigner's revolutionary vision predicted that the energy levels of large complex quantum systems exhibit a universal behavior: the statistics of energy gaps depend only on the basic symmetry type of the model. Simplified models of…

Mathematical Physics · Physics 2012-12-05 Laszlo Erdos

We investigate the relation between different three-dimensional reductions of the Bethe-Salpeter equation and the analytic structure of the resultant amplitudes in the energy plane. This correlation is studied for both the $\phi^2\sigma$…

Nuclear Theory · Physics 2009-11-07 A. D. Lahiff , I. R. Afnan

Understanding how much each variable contributes to an outcome is a central question across disciplines. A causal view of explainability is favorable for its ability in uncovering underlying mechanisms and generalizing to new contexts.…

Methodology · Statistics 2026-03-09 Weihan Zhang , Zijun Gao

Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains.…

Machine Learning · Computer Science 2026-05-27 Nikita Dhawan , Arnav Paruthi , Andrew Kim , Lovedeep Gondara , Jekaterina Novikova , Chris J. Maddison

The Wigner function formalism which relates source size parameters to experimental ``HBT radii'' extracted from two-particle Bose-Einstein correlations is generalized to azimuthally deformed and longitudinally tilted sources. It is…

Nuclear Theory · Physics 2009-10-09 Ulrich W. Heinz

Adjusting for covariates is a well established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study there may be different adjustment…

Statistics Theory · Mathematics 2021-04-27 Jack Kuipers , Giusi Moffa

Motivated by the limited interaction between the mathematical physics community and theoretical physicists - particularly in high-energy theory - we present a computation that is typically the first example in QFT courses but, to our…

High Energy Physics - Theory · Physics 2025-09-26 H. A. C. Grande , J. C. A Barata

We take causality and uniqueness of events observation as our driving forces. They are built in in the way we define distinct observers, which then require a finite time to communicate between each other. This unavoidably leads to the…

General Physics · Physics 2026-04-09 Antonio Pineda

Using the principle of causality as expressed in the Kramers-Kronig relations, we derive a generalized criterion for a negative refractive index that admits imperfect transparency at an observation frequency $\omega$. It also allows us to…

Optics · Physics 2008-12-09 P. Kinsler , M. W. McCall

Evidence is provided for a conjecture that, in the continuum limit, the mean of the causal set action of a causal set sprinkled into a globally hyperbolic Lorentzian spacetime, M, of finite volume equals the Einstein Hilbert action of M…

General Relativity and Quantum Cosmology · Physics 2020-07-29 Fay Dowker

When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target population. One challenge is that the covariate distribution in…

Methodology · Statistics 2025-01-03 Zhenghao Zeng , Edward H. Kennedy , Lisa M. Bodnar , Ashley I. Naimi

Complex dynamical systems are prevalent in many scientific disciplines. In the analysis of such systems two aspects are of particular interest: 1) the temporal patterns along which they evolve and 2) the underlying causal mechanisms.…

Methodology · Statistics 2022-05-31 Nicolas-Domenic Reiter , Andreas Gerhardus , Jakob Runge

We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pivotal role in uncovering directional relationships among…

Machine Learning · Statistics 2024-10-21 Juraj Bodik , Olivier C. Pasche
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