This post tries to unsettle some of the methods used in economics today – Regression analysis and Granger causality. Apart from this objective, the post also tries to understand the meaning (rather, meanings) of causality. Do we economists mistake correlation for causality? Can we have a single method for capturing causality?
Causation is defined in the following ways:
-the action of causing or producing.
-the relation of cause to effect; causality
-anything that produces an effect; cause [Dictionary.com]
And the definition of Correlation is:
-mutual relation of two or more things, parts, etc. [Dictionary.com]
I once asked a professor who had offered to give a lecture for our Advanced Economic Theory class this: “Sir, is it possible to establish causation conclusively?” He replied “That is simple. There are these tests- Granger test, Sims test, Sargent test, McClave-Hsiao test, Haugh-Pierce test, etc.” And he wrote the names of these so-called ‘scientific’ tests on our black board.
What we often forget is that, there is no single and simple understanding of causation. There are various kinds of causality like epistemic, conceptual, factual, counterfactual etc. Causality in economics also are of different natures- poverty is causing unemployment, increased demand for oil has resulted in oil price rise, supply constraints are hiking up the price, etc. For instance, conceptually we know that poverty causes unemployment (vicious cycle of poverty) and that increased demand causes a concomitant price rise. In economics, it is important to have an account of both conceptual as well as factual causality.
For causality to be present between two variables A and B, it is necessary for them to be related in some way. This relationship among them can be of a linear nature or a non-linear one. If it is linear in nature, then it is called correlation. [Note that regression analysis (OLS) is based on correlation and is linear in nature.] But, correlation alone does not imply causation. Hence, all those who think that causation and correlation are the same make an inductive leap – from correlation to that of causation.
As R G D Allen writes in his Statistics for Economics: “This statistical concept of correlation is quite neutral as regards causation. One of the variables may be ’caused’ by the other, but this can only be known from other than statistical considerations.” Usually, causal hypotheses are derived from economic theory, because ‘data does not speak on its own’. We need to pass data through theory in order to make sense of it.
However, economic theory (like any other theory) contains a lot of assumptions, mostly unrealistic. What happens to causality then? Causality, then is dependent on these assumptions. Hence, drawing inferences from such theoretical models for practical purposes should be undertaken with caution.
It is this caution that seems to be missing amongst econometricians. This will be evident after looking into the workings of Granger causality. Granger causality analyses probabilistic causality. However, this per se is not a limitation of the test. For, in social sciences, it is exceedingly difficult to talk about deterministic causes in real-world scenario- especially, in a macroeconomic environment.
If X and Y are probabilistically dependent and X precedes Y, then X causes Y.
And, in actual testing, Y is regressed on X (t) and also on X (t-1). If the latter regression is found to be more significant, then X is said to Granger-cause Y. In actual practice, there are economists who forget the prefix. That is, again, some sort of correlation analysis is carried out between Y and X (t-1). In any case, the concept of causation is a problem-ridden one. And as economists who contribute to policy-making, one ought to be on their toes all the time.