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# What is Causality

The relationship between cause and effect (result). In system dynamics, there are two types of causality: positive and negative. Positive causality means a result element changes to the same direction as its cause element changes to. Negative causality indicates a result element changes to the opposite direction to its cause element’s change. This is also called a causal relationship.
Published in Chapter:
System Dynamics
Yutaka Takahashi (School of Commerce, Senshu University, Japan)
DOI: 10.4018/978-1-4666-5888-2.ch120
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This concept shows a relationship between two events where one event is affected by the other. Statistically, if one variable, increases or decreases as a result of other variable, then there is causation exists.
It is the method used in time series econometrics where the short run interplays among two or more variables are studied. In a two variable model Granger causality test is run but in a VAR model Wald test is applied to test for causality.
The method that is used to whether an independent variable predicts the dependent variable’s further values or not.
Given a relationship between two or more variables, it looks the relationship between cause and effect or what variables cause what.
The relationship between a cause and an effect.
The association between two events or states such that one generates or brings about the other, where one is the cause and the other its effect.
Causality (also referred to as causation, or cause and effect) is the agency or efficacy that connects one process (the cause) with another process or state (the effect), where the first is understood to be partly responsible for the second, and the second is dependent on the first.
Causality (also referred to as causation, or cause and effect) is the agency or efficacy that connects one process (the cause) with another process or state (the effect), where the first is understood to be partly responsible for the second, and the second is dependent on the first.
To give statistically significant information on a time series values about the future values of another time series.
The relationship that exists between two factors and show a cause and effect; one factor that because it occurs causes a second factor to always occur and can be predictive.
A term to describe cause and effect. Several types of causality are recognized: linear (x causes y and a doubling in x causes a doubling in y), circular (x causes a change in y which in turn causes a change in x), recursive (x is caused by the system that is x – society is made of people that bear children that make up society), nonlinear (as opposed to linear the change in x causes a change in y that follows a power law, for example).
If a time series is said to cause, or lead, another time series in the short run, or vice versa, there exists a causal relationship among the series, meaning that these time series affect each other in the short run.
The causal relationship between two variables.
It is a time series econometric method of testing directions of cause and effect between two or more variables.