Correlation and Causality by David A. Kenny

By David A. Kenny

A normal advent to the subject of structural research that presumes no earlier acquaintance with causal research. Assumes reader has a few familiarity with a number of regression and issue research.

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Some early path analysts stated that the total effect is simply the correlation between the effect and the causal variable. However, a little reflection shows that the correlation between variables reflects more than direct and indirect effects. If the causal variable is exogenous, then part of its correlation with the endogenous variable may be due to its correlation with the other exogenous variables and the effects of these exogenous variables.

The semipartial is the effect of a disturbance of one endogenous variable on another endogenous variable. At present, to identify this path it must be assumed that neither of the endogenous variables causes the other. Note that the semipartial is a nonsymmetric measure of association. 3). The semipartial is useful in the computation of regression coefficients in stepwise regression. We should not confuse this usefulness with its causal interpretation. The semipartial has not and probably should not have much role in structural analysis.

I would not argue for exactly this solution. It is possible that there is a specification error of a path from X1 to X3. If there is such a path, the estimate of the path from X2 to X3 will be distorted through this specification error. It might be wiser to regress X3 on both X1 and X2 and to expect the path from X1 to X3 to be nonsignificant. 5 Causal chain. 13 must hold. Thus, he overidentifying restriction is tested by the prediction of the zero path. A price is paid for this strategy. The variance of the estimate of b is increased, but at least the researcher knows that b is not biased if X1 causes X3.

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