Granger causality

The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether X causes Y, the Granger causality tests whether X forecasts Y. A time series X is said to Granger-cause Y if it can b

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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

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

Micropollutant-induced tolerance of in situ periphyton: Establishing causality in wastewater-impacted streams

Renata Behra, Juliane Hollender, Ahmed Tlili

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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Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may i

Causality

Causality (also called causation, or cause and effect) is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (a

Correlation does not imply causation

The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed a