Correlation Causation

The Correlation analysis helps us in determining the degree of relationship between two or more variables-it does not tell us anything about cause and effect relationship. Even a high degree of correlation does not necessarily mean that a relationship of cues and effect exists b/w the variables or, simple stated, correlation does not necessarily imply causation or functional relationship though the existence of causation always implies correlation. By itself, it establishes only the coronation. It always implies the correlation. By itself it establishes only covariation. The explanation of a significant degree of correlation may be any one, or the combination of the following reasons:

(i) The correlation may be due to the pure chance, especially in a small sample. We may get a high degree of correlation b/w two variables in a sample but in the universe there may not be any relationship b/w the variables at all. This is especially in the case of small samples. Such a correlation may arise either as of pure random sampling variation or because of the bias of the investigator in selecting the sample. The following example shall elaborate the point:

The data above show a perfect positive relationship between income and weight, i.e. as the income is increasing the weight and the rate of change between and the rate of change between two variables are same.

(ii) Both the correlated variables may influenced by one or more other variables. It is possible that a high degree of correlation between the variables may be due to some causes affecting each with the same effect. For e.g., a high degree of correlation between the yield per acre of tea and rice may be due to the fact that both are related to the amount of rainfall. But no one of the two variables is the cause of the other. Let's take another example: Assume that the correlation of teacher's salaries and the consumption of liquor over a period of year come out to be 0.9, this does not prove that teachers drink; nor does it prove that liquor sale increases teacher's salaries. While, both variables move together as both are influenced by a third variable- Long run growth in national income and population.

(iii) Both the variables may be mutually influencing to each other so that neither can be designated as he cause and the other effect. There may be a high degree of correlation between the variables but it may be very difficult to pinpoint as to which is the cause and which is the effect. This is especially likely to be so in the case of which is the case economic variables. For e.g., such variables as price and protection, demand and supply,   mutually interact etc. To take a specific case, it is well known principle of economics that as the price of a commodity increases its demand goes down and so price is the cause and demand of a commodity due to growth of population or other reasons may exercise an upward pressure on price. Thus, at times it may become more difficult to explain from the two correlated variables which is the cause and which is the effect as both may be resting on each other.

The above points clearly bring out the fact that a mathematical relationship implies nothing in itself about cause & effect. In normal, if factors A and B are correlated, it may be that (1) a causes to be sure but it might also be that (2) b causes a, (3) a and b influence each other continuously or intermittently. (4) A and B are both influenced by C or (5) the correlation is due to the chance. In many instances extremely high degree of correlation between the two variables may be obtained when no meaning can be attached to the answer. There is, for example extremely high correlation between some series showing the production of pigs and the production of pig iron, yet no one has ever believed that this correlation has any meaning or that it shows the existence of a cause-effect relation. By itself, it only establishes covariation. The Correlation observed between variables that cannot conceivably be casually related is termed spurious of nonsense correlation more appropriately; we must remember that it is the interpretation of the degree of correlation that is spurious, not the degree of correlation itself. The high degree of correlation shows only the mathematical result. We should reach on a conclusion depend on logical reasoning and intelligent investigation of significantly related matters, it only reading causation into spurious correlation but also interpreting spuriously a perfectly valid relationship.