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A causal relationship indicates “a relationship that can be said to have a cause-and-effect relationship between items.”
For example, when examining the relationship between advertising expenses and sales, it is generally accepted that “increasing advertising expenses increases sales.” The act of “increasing advertising expenses” leads to “increasing sales;” therefore, there is a causal relationship between the two items.
The relationship between cause and effect is based on the temporal order of “there is a cause and there is an effect.”
In addition, regarding the relationship between height and weight, it is not clear whether taller people are heavier or heavier people are taller; therefore, the causal relationship between the two is uncertain.
If a causal relationship exists, there is always a correlation. However, a correlation does not necessarily indicate a causal relationship.
As a correlation does not indicate a causal relationship, we must examine the temporal order of the items to identify a possible causal relationship. Therefore, further statistical analysis is required to determine whether a causal relationship exists between two correlated variables. A typical statistical analysis method is the covariance structure analysis.
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