Sentence examples for causality based from inspiring English sources

Most of the empirical applications and tests for causality rely on using Granger causality based test for linear models.

Second, as we aim to provide causal estimates, we document if the estimated effects are sensitive to the identification strategies that we use to claim causality based on observational data.

This paper suggests how the contemporary theory of causality, based on the notions of counterfactuality and contrastive thinking, offers helpful direction on how to generate plausible causal arguments in interpretive research.

For example, the causality based support system known as a robust representation framework for a single biological system, stores information of a biological system only by its internal 'physical relations' and retrieves biological systetabms only by the physical relevance.

In this research, a causality based holistic representation framework for biological systems and an 'all-connected' ontology based repository and retrieval system are developed as a knowledge-based recommendation system to support bio-inspired design.

The purpose of this paper is to propose a newly developed non-parametric test for linear and nonlinear causality based on permutation entropy and to show its usefulness in analyzing the potential causal relationship between trading volume and security prices.

Tests for unit roots, co-integration, and Granger-causality based on error-correction model are presented.

This is the traditional Granger-Causality (based on a bi-variate relationship).

To capture the influence of financial innovation on economic growth, we estimated the long-run cointegration by applying Autoregressive Distributed Lag (ARDL) bound testing and Granger causality-based Error Correction Model (ECM) to capture the directional association.

In other words, we will derive the Granger causality-based functional clustering directly from the time series gene expression data.

This is a type of predictive causality test based on causal ordering.