Causality, Correlation and Regression

This video will explain you the commonalities and differences between the correlation, regression and the causality.
Causality means that there is a clear cause-effect relationship between two variables.
A common mistake in the interpretation of statistics is that when a correlation exists a causality is inferred.
There are two prerequisites for causality:
First, there is a significant relationship, that is, a Significant Correlation.
The second condition can be satisfied in two ways.
First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B.
Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship goes.
If neither of the two is true, i.e. there is neither a temporal order nor can the causality be justified by a well-founded theory, then we can only speak of a relationship, but never of causality, i.e. it cannot be said that variable A influences variable B or vice versa.
More Information about Causality:
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