November 20, 2011

1.4 REGRESSION VERSUS CAUSATION (Damodar N. Gujarati)

Although regression analysis deals with the dependence of one variable on other variable, it does not necessarily imply causation. the words of Kendall and Stuart,"A statistical relationship, however strong an however suggestive, can never establish causal connection: our ideas of causation must com from outside statistics, ultimately from theory or other."5
In the corp-yield example cited previously, there is no statistical reason to assume that rainfall does not depend on corp yield. The fact that we treat crop yield considerationas dependent on rainfall ( among other things ) is due to non statistical consideration: Common sense suggests that the relationship cannot be reversed, for we cannot control rainfall by vaying crop yield.
In all the example cited in Section1.2 the point to note  is that a statistical relationship in self cannot logically imply causation. To ascribe causality , one must appeal to a priori or theoretical consideration. thus,in the third example cited, one can invoke economic theory in saying that consumption expenditure depends on real income.6
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5 M.G.Kendall an A. Stuart, The Advanced Theory of Statistics, Charles Griffin Publishers. New York,1961, vol. 2. chap.26,p.279.
6But as we shall see in Chap.3, classical regression analysis is based on the assumption that the model used in the analysis is the correct model. Therefore, the direction of causality may be implicit in the model postulated

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