Causalité au sens de Granger

Résumé

La causalité a été introduite dans l'analyse économétrique par Wiener (1956) et Granger (1969). À l'origine, on retrouve la formalisation de la notion de causalité en physique, notamment dans les travaux d'Isaac Newton sur la force motrice (cause) et le changement de mouvement (effet). Dans ce cas, la notion de causalité traduit un principe d’après lequel si un phénomène est la cause d’un autre phénomène, nommé « effet », alors ce dernier ne peut pas précéder la cause. Cependant, sa définition conceptuelle remonte aux discours d'Aristote ou de David Hume. center|thumb|upright=1.4|Matrice de causalité de Granger avant le test de significativité Transposée en économie, la notion de causalité revêt une connotation technique spécifique. En effet, si une variable causait une autre variable, alors nécessairement les deux variables doivent être corrélées. À l'inverse, il ne suffit pas que deux variables soient corrélées, pour qu’il ait causalité (corrélation n'est pas causalité). center|t

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https://fr.wikipedia.org/wiki/Causalit%C3%A9_au_sens_de_Granger

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En probabilités et en statistique, la corrélation entre plusieurs variables aléatoires ou statistiques est une notion de liaison qui contredit leur indépendance. Cette corrélation est très souvent

vignette|Exemple classique de la chute d'un domino causé par la chute d'un autre. En science, en philosophie et dans le langage courant, la causalité désigne la relation de cause à effet.

la cause,

Cum hoc ergo propter hoc (latin signifiant avec ceci, donc à cause de ceci) est un sophisme qui consiste à prétendre que si deux événements sont corrélés, alors, il y a un lien de cause à effet entre

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Gravitational causality and the self-stress of photons

Giulia Isabella

We study causality in gravitational systems beyond the classical limit. Using on-shell methods, we consider the 1-loop corrections from charged particles to the photon energy-momentum tensor - the self-stress - that controls the quantum interaction between two on-shell photons and one off-shell graviton. The self-stress determines in turn the phase shift and time delay in the scattering of photons against a spectator particle of any spin in the eikonal regime. We show that the sign of the beta-function associated to the running gauge coupling is related to the sign of time delay at small impact parameter. Our results show that, at first post-Minkowskian order, asymptotic causality, where the time delay experienced by any particle must be positive, is respected quantum mechanically. Contrasted with asymptotic causality, we explore a local notion of causality, where the time delay is longer than the one of gravitons, which is seemingly violated by quantum effects.

2022

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Joint Fusion Learning of Multiple Time Series Prediction

Orçun Gümüs

Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the transportation industry numerous possibilities to improve their operations using data analyses on traffic flow sensor data . However, even state-of-art algorithms for time series forecasting perform well on some transportation problems, they still fail to solve some critical tasks. In particular, existing traffic flow forecasting methods that are not utilising causality relations between different data sources are still unsatisfying for many real-world applications . In this report, we have focused on a new method named joint fusion learning that uses underlying causality in time series. We test our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we use our joint-fusion learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT) . We obtained better results on the short-term forecasts compared the widely-used benchmarks models that uses single time series to forecast the future.

2019

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, ,

The overarching aim of this field study was to examine causal links between in-situ exposure to complex mixtures of micropollutants from wastewater treatment plants and effects on freshwater microbial communities in the receiving streams. To reach this goal, we assessed the toxicity of serial dilutions of micropollutant mixtures, extracted from deployed passive samplers at the discharge sites of four Swiss wastewater treatment plants, to in situ periphyton from upstream and downstream of the effluents. On the one hand, comparison of the sensitivities of upstream and downstream periphyton to the micro pollutant mixtures indicated that algal and bacterial communities composing the periphyton displayed higher tolerance towards these micropollutants downstream than upstream. On the other hand, molecular analyses of the algal and bacterial structure showed a clear separation between upstream and downstream periphyton across the sites. This finding provides an additional line of evidence that micropollutants from the wastewater discharges were directly responsible for the change in the community structure at the sampling sites by eliminating the micropollutant-sensitive species and favouring the tolerant ones. What is more, the fold increase of algal and bacterial tolerance from upstream to downstream locations was variable among sampling sites and was strongly correlated to the intensity of contamination by micropollutants at the respective sites. Overall, our study highlights the sensitivity of the proposed approach to disentangle effects of micropollutant mixtures from other environmental factors occurring in the field and, thus, establishing a causal link between exposure and the observed ecological effects on freshwater microbial communities. (C) 2017 Elsevier Ltd. All rights reserved.

Elsevier2017

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