Search results for "causality"
showing 10 items of 258 documents
Causalidad en salud laboral: el caso ardystil
1995
ResumenEl establecimiento de relaciones causa-efecto ha sido y sigue siendo objeto de debate en epidemiología. La naturaleza observacional de la investigación epidemiológica dificulta el reconocimiento de estas relaciones. En este contexto, se han propuesto diferentes modelos para explicar las relaciones causales en los procesos de salud y enfermedad, desde el modelo determinista puro defendido por los postulados de Koch, que requiere la aceptación de relaciones unicausales, pasando por otras explicaciones que asumen la naturaleza multicausal de los problemas de salud en la población. En Salud Laboral se debe disponer también de modelos explicativos y criterios consensuados para valorar las…
A new Framework for the Spectral Information Decomposition of Multivariate Gaussian Processes
2021
: Different information-theoretic measures are available in the literature for the study of pairwise and higher-order interactions in multivariate dynamical systems. While these measures operate in the time domain, several physiological and non-physiological systems exhibit a rich oscillatory content that is typically analyzed in the frequency domain through spectral and cross-spectral approaches. For Gaussian systems, the relation between information and spectral measures has been established considering coupling and causality measures, but not for higher-order interactions. To fill this gap, in this work we introduce an information-theoretic framework in the frequency domain to quantify t…
Data from: The strategic reference gene: an organismal theory of inclusive fitness
2019
How to define and use the concept of inclusive fitness is a contentious topic in evolutionary theory. Inclusive fitness can be used to calculate selection on a focal gene, but it is also applied to whole organisms. Individuals are then predicted to appear designed as if to maximise their inclusive fitness, provided that certain conditions are met (formally when interactions between individuals are ‘additive’). Here we argue that applying the concept of inclusive fitness to organisms is justified under far broader conditions than previously shown, but only if it is appropriately defined. Specifically, we propose that organisms should maximise the sum of their offspring (including any accrued…
Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality
2015
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…
Taking historical embeddedness seriously : Three historical approaches to advance strategy process and practice research
2016
International audience; Despite the proliferation of strategy process and practice research, we lack understanding of the historical embeddedness of strategic processes and practices. In this paper, we present three historical approaches with the potential to remedy this deficiency. First, realist history can contribute to a better understanding of the historical embeddedness of strategic processes; in particular, comparative historical analysis can explicate the historical conditions, mechanisms, and causality in strategic processes. Second, interpretative history can add to our knowledge of the historical embeddedness of strategic practices, and microhistory can specifically help to under…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Estimation of brain connectivity through Artificial Neural Networks
2019
Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…
Multiscale Granger causality analysis by à trous wavelet transform
2017
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…
Bayesian Metanetwork for Context-Sensitive Feature Relevance
2006
Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a “relevance” of the predictive attributes towards tar…
The limits of the rotating wave approximation in electromagnetic field propagation in a cavity
2005
We consider three two-level atoms inside a one-dimensional cavity, interacting with the electromagnetic field in the rotating wave approximation (RWA), commonly used in the atom-radiation interaction. One of the three atoms is initially excited, and the other two are in their ground state. We numerically calculate the propagation of the field spontaneously emitted by the excited atom and scattered by the second atom, as well as the excitation probability of the second and third atom. The results obtained are analyzed from the point of view of relativistic causality in the atom-field interaction. We show that, when the RWA is used, relativistic causality is obtained only if the integrations …