Concept of Causality and Causal Variables

Causality is the relationship between two variables, the first being cause and the second being effect. There are two types of causality relationship between these variable, bidirectional causality and unidirectional causality. The relationship between these two variables should be either unidirectional or bidirectional.

Unidirectional causality & bidirectional causality:

Cause is an Independent Variable (IV), whereas Effect is a Dependent Variable (DV)

In cause effect relationship, we will always test sufficient condition first, because if sufficient condition is present then this means that necessary condition will automatically has to be present. For example, consider Cause variable as Clouds and effect variable as rain. The effect variable, that is, rain will also be known as sufficient condition. So in this model we will check if rain is present or not. If suppose we can see that rain is present, then it is automatically necessary that there must be cloud present due to which rain occurred, therefore the presence of cloud is known as necessary condition. We can conclude that if sufficient condition is true, then automatically necessary condition has to be true.

In bidirectional causality, Cause variable causes effect variable, however, at the same time effect variable also causes Cause variable. This means both reactions can take place simultaneously.

Unidirectional Causality and Granger Causality Test:

Condition for unidirectional cause variables are:

1. Both variables should be in time series.
2. Both variable should have shocks (non-Stationarity).
3. In both time series, shocks should be fixed by the help of 1^st or 2^nd
4. AR – process should be present.
5. GARCH should be significant, that is, volatility should also be present.
6. Number of variable should be equal to two.

The test used to check unidirectional causality is known as Granger Causality Test.

Bidirectional causality and Cross-correlation Test:

Condition for bidirectional cause variables are:

1. Both variables should be in time series.
2. Both variable should have shocks (non-Stationarity).
3. In both time series, shocks should be fixed by the help of 1^st or 2^nd
4. AR – process should be present.
5. GARCH should be significant, that is, volatility should also be present.
6. Number of variable should be equal to two.
7. There should be a significant unidirectional causality between both time series variables.

The test used to check bidirectional causality is known as Cross-correlation Test.

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