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Related papers: Causality and the effective range expansion

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Many years ago Weinberg formulated a definition of ``naturalness'' for effective theories: if an effective theory is to make sense, coefficients must not change too much when the cutoff scale is changed by a factor of order 1. As an…

High Energy Physics - Phenomenology · Physics 2013-11-13 Michael J. Dugan , Mitchell Golden

In applied research, Lee (2009) bounds are widely applied to bound the average treatment effect in the presence of selection bias. This paper extends the methodology of Lee bounds to accommodate outcomes in a general metric space, such as…

Econometrics · Economics 2026-01-15 Daisuke Kurisu , Yuta Okamoto , Taisuke Otsu

Making causal inferences from observational studies can be challenging when confounders are missing not at random. In such cases, identifying causal effects is often not guaranteed. Motivated by a real example, we consider a…

Methodology · Statistics 2023-10-31 Jian Sun , Bo Fu

This article proposes a systematic methodological review and objective criticism of existing methods enabling the derivation of time-varying Granger-causality statistics in neuroscience. The increasing interest and the huge number of…

Applications · Statistics 2017-04-12 Sezen Cekic , Didier Grandjean , Olivier Renaud

This work is concerned with finite range bounds on the variance of individual eigenvalues of Wigner random matrices, in the bulk and at the edge of the spectrum, as well as for some intermediate eigenvalues. Relying on the GUE example,…

Probability · Mathematics 2012-07-06 Sandrine Dallaporta

I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample regime. I first consider a multi-dimensional variable…

Machine Learning · Statistics 2019-07-01 Dominik Janzing

In this paper, we explore positivity bounds for the effective field theory~(EFT) of a single weakly coupled massive vector field. The presence of both mass and spin makes the crossing properties of the amplitudes vastly complicated -- we…

High Energy Physics - Theory · Physics 2025-07-16 Francesco Bertucci , Johan Henriksson , Brian McPeak , Sara Ricossa , Francesco Riva , Alessandro Vichi

Causal structures give us a way to understand the origin of observed correlations. These were developed for classical scenarios, but quantum mechanical experiments necessitate their generalisation. Here we study causal structures in a broad…

Quantum Physics · Physics 2021-06-30 Mirjam Weilenmann , Roger Colbeck

The method of effective field theories (EFTs) is developed for the scattering of two particles at wavelengths which are large compared to the range of their interaction. It is shown that the renormalized EFT is equivalent to the effective…

Nuclear Theory · Physics 2008-11-26 U. van Kolck

I apply the scattering approach within the framework of macroscopic quantum electrodynamics to derive the variances and mean values of the energy density and intensity for a system of an arbitrary object in an arbitrary environment. To…

Mesoscale and Nanoscale Physics · Physics 2023-12-15 Florian Herz

Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables…

Artificial Intelligence · Computer Science 2023-01-18 Sander Beckers , Joseph Y. Halpern , Christopher Hitchcock

Although understanding and characterizing causal effects have become essential in observational studies, it is challenging when the confounders are high-dimensional. In this article, we develop a general framework $\textit{CausalEGM}$ for…

Machine Learning · Statistics 2023-03-20 Qiao Liu , Zhongren Chen , Wing Hung Wong

We derive a generalized Low equation for the T-matrix appropriate for complex atom-molecule interaction. The properties of this new equation at very low energies are studied and the complex scattering length and effective range are derived.

Atomic Physics · Physics 2009-11-06 M. S. Hussein

Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data…

Methodology · Statistics 2014-07-03 Juha Karvanen

In this work, we summarize the state-of-the-art methods in causal inference for extremes. In a non-exhaustive way, we start by describing an extremal approach to quantile treatment effect where the treatment has an impact on the tail of the…

Methodology · Statistics 2024-03-11 Valérie Chavez-Demoulin , Linda Mhalla

In this paper, we introduce a unified estimator to analyze various treatment effects in causal inference, including but not limited to the average treatment effect (ATE) and the quantile treatment effect (QTE). The proposed estimator is…

Methodology · Statistics 2025-03-31 Kuan-Hsun Wu , Li-Pang Chen

We generalize Batchelor's parameterization of the autocorrelation functions of isotropic turbulence in a form involving a product expansion with multiple small scales. The richer small scale structure acquired this way, compared to the…

Fluid Dynamics · Physics 2015-01-13 Elias Gravanis , Evangelos Akylas

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

The validity range of the time-honored effective range expansion can be very limited due to the presence of a left-hand cut close to the two-particle threshold. Such a left-hand cut arises in the two-particle interaction involving a light…

High Energy Physics - Phenomenology · Physics 2025-07-04 Meng-Lin Du , Feng-Kun Guo , Bing Wu

The interaction of two binary variables, assumed to be empirical observations, has three degrees of freedom when expressed as a matrix of frequencies. Usually, the size of causal influence of one variable on the other is calculated as a…

Artificial Intelligence · Computer Science 2014-04-22 David A. Eubanks