Causality

Causality is the relationship between cause and effect. A causal relationship usually requires: the existence of a spatial and temporal contiguity between two events, the occurrence of one event before the other, and the unlikelihood that the second event could have occurred unless the other event occurred first. A causal relationship is also often thought to exist when a particular type of event always or almost always occurs in a particular way. Such a lawlike relationship, however, is not necessarily evidence of a direct causal relationship but may only show a correlation.

Causality usually involves both an immediate cause and some more underlying cause. For example, the immediate cause of the First World War was the assassination of Archduke Ferdinand; an underlying cause was nationalist tension exacerbated by the European arms race. To determine the relative importance of such multiple causes and to distinguish correlation from causality, sociologists have developed various analytical systems, including multivariate analysis and causal modeling. In addition, sociologists consider whether causality associated with purposive actions follows the same rules as does causality in physical science. Another question of interest is whether functional explanation is a type of causal analysis. (Susser, 635-648)

Many theorists consider concepts of causation to be flawed. Philosophers argue that causation does not easily fit with classical conceptions of logic because it identifies as causal some conditions that may not be necessary or sufficient to create the effect in question. And epistemologists point out that it is scientifically impossible to state conclusively that any given relationship is necessarily causal.

The objective of any causal analysis is to try to influence the degree of belief held by an individual about the correctness of some causal theory. Hence, the task of the analysis is not to be complete in itself, but rather to have enough value to make one consider one's belief. There are basically two types of causal testing situations. In a crosssectional causality analysis the question asked is why this variable behaves differently from the other. In a temporal causality analysis the question asked is why this variable changes behavior from period to period. Although many important economic questions can be phrased in the cross-section causal situation, they have received little causal testing in that context and many tests have been conducted for economic questions that can be stated as temporal causation. The definitions of causality and their interpretations may differ between cross-section and time-series cases. In all cases, however, the classification of variables into exogenous and endogenous and the causal structure of the mechanism (econometric model) are under scrutiny. (Evans, 142-145)

The relation between exogeneity and causality is the heart of any investigation into causal analysis. There are a number of definitions of exogeneity: weak, super, and strong exogeneity. A variable is said to be weakly exogenous for estimating a set of parameters if inference on the parameters conditional on this exogenous variable involves no loss of information. The concept of superexogeneity is related to the Lucas critique, which states that if a variable is weakly ...