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Causality in Economics

Causality (root word 'cause') determines what cause of an effect (or consequence) is. And in order to make a rational decision, the economist must know the independent variable and the dependent variable, parts of a choice leading to a utility, where dependent variable is a consequence of independent variable.

Another way of viewing causality is finding the existence of causality in a given relation or function of two variables, or parameters. It happens, as we are told, that there may be existent an association between two things/events/choices yet no causality is to be found in the ‘relation’. Word ‘relation’ is important here. Because, here we’re not discussing the matter from the view of classical, philosophical cause-effect theory, rather we are strictly concerned with the mathematical relationship between different variables.

In the following paragraph, I’ll show you two different cases where both variables are associated with each other on the graph. You may notice at the end that intuition works better at the basic level. But the importance of rigorous analysis and empirical research shall also be highlighted by the demonstration of idea.

Consider this case: ‘Sugar’ is a dependent variable. ‘Sugarcane’, on the other hand is an independent variable. There is also a natural (that is to assume quite intuitively) association or relation between these two variables. And sugar is dependent to sugarcane (independent), because we cannot think of sugar, without any sugarcane. Therefore, existence of sugar depends on that of sugarcane. Or in other words, sugarcane is the cause of sugar (effect of sugarcane).

Sugar = f(sugarcane, etc) [Sugar is a function of Sugarcane]

Consider the second case where an association between two things is present with the absence of causality given their mode of function

If I draw a graph and show on x-axis 'number of police constables' recruited in a given period, in which the number of crimes taking place in the city had increased. Crimes data shown on y-axis, while police data shown on x-axis. Meaning by, that crime is a cause of increase in the number of police constables. Police and crime have a relation and association no human being can deny. Yet the idea seems absurd. Because police helps diminish the crime, not vice verse. So intuitively we conclude that although there is an association between the idea of police-crime, there is no causality (that police is a cause of increase in crime) established between them.

Here comes the interesting part of the game. Rigorous mathematical and statistical analysis of these two ideas, so that it could be made ascertain where does valid causality lies. Regression models ar built, in-depth research is carried out, many reports are made to establish the truth. Intuition is just the beginning and in the middle, sometimes it is not the end in itself. Sometimes.

(Warning note: This post had no intention towards public benefit. It was a personal matter. And was addressed to my own self. If you have questions to ask please contact the economist-teacher Mr. N. Gregory Mankiw of Harvard University, U.S.A. But if you've answers and something to educate and share, can't you just go ahead and do it, please?)

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