## What can econometrics achieve?

from **Lars Syll**

A popular idea in quantitative social sciences is to think of a cause (C) as something that increases the probability of its effect or outcome (O). That is:

P(O|C) > P(O|-C)

However, as is also well-known, a correlation between two variables, say A and B, does not necessarily imply that that one is a cause of the other, or the other way around, since they may both be an effect of a common cause, C.

In statistics and econometrics we usually solve this “confounder” problem by “controlling for” C, i. e. by holding C fixed. This means that we actually look at different “populations” – those in which C occurs in every case, and those in which C doesn’t occur at all. This means that knowing the value of A does not influence the probability of C [P(C|A) = P(C)]. So if there then still exist a correlation between A and B in either of these populations, there has to be some other cause operating. But if *all* other possible causes have been “controlled for” too, and there is still a correlation between A and B, we may safely conclude that A is a cause of B, since by “controlling for” all other possible causes, the correlation between the putative cause A and all the other possible causes (D, E,. F …) is broken.

This is of course a very demanding prerequisite, since we may never actually be sure to have identified *all* putative causes. Even in scientific experiments may the number of uncontrolled causes be innumerable. Since nothing less will do, we do all understand how hard it is to actually get from correlation to causality. This also means that *only* relying on statistics or econometrics is not enough to deduce causes from correlations.

Some people think that randomization may solve the empirical problem. By randomizing we are getting different “populations” that are homogeneous in regards to all variables except the one we think is a genuine cause. In that way we are supposed being able not having to actually know what all these other factors are.

If you succeed in performing an *ideal* randomization with different treatment groups and control groups that is attainable. *But* it presupposes that you really have been able to establish – and not just assume – that the probability of all other causes but the putative (A) have the same probability distribution in the treatment and control groups, and that the probability of assignment to treatment or control groups are independent of all other possible causal variables.

Unfortunately, *real *experiments and *real* randomizations seldom or never achieve this. So, yes, we may do without knowing *all *causes, but it takes *ideal* experiments and *ideal* randomizations to do that, not *real *ones.

That means — as I have argued (here) — that in practice we do have to have sufficient background knowledge to deduce causal knowledge. Without old knowledge, we can’t get new knowledge – and, no causes in, no causes out.

As I have written about earlier (here and here), Keynes was very critical of the way statistical tools were used in social sciences. In his criticism of the application of inferential statistics and regression analysis in the early development of econometrics, Keynes in a critical review of the early work of Tinbergen, writes:

Prof. Tinbergen agrees that the main purpose of his method is to discover, in cases where the economist has correctly analysed beforehand the qualitative character of the causal relations, with what -strength each of them operates. If we already know what the causes are, then (provided all the other conditions given below are satisfied) Prof. Tinbergen, given the statistical facts, claims to be able to attribute to the causes their proper quantitative importance. If (anticipating the conditions which follow) we know beforehand that business cycles depend partly on the present rate of interest and partly on the birth-rate twenty years ago, and that these are independent factors in linear correlation with the result, he can discover their relative importance. As regards disproving such a theory, he cannot show that they are not verce causce, and the most he may be able to show is that, if they are verce cause, either the factors are not independent, or the correlations involved are not linear, or there are other relevant respects in which the economic environment is not homogeneous over a period of time (perhaps because non-statistical factors are relevant). Am I right in thinking that the method of multiple correlation analysis essentially depends on the economist having furnished, not merely a list of the significant causes, which is correct so far as it goes, but a

completelist? For example, suppose three factors are taken into account, it is not enough that these should be in fact verce causce; there must be no other significant factor. If there is a further factor, not taken account of, then the method is not able to discover the relative quantitative importance of the first three. If so, this means that the method is only ap-plicable where the economist is able to provide beforehand a correct and indubitably complete analysis of the significant factors. The method is one neither of discovery nor of criticism. It is a means of giving quantitative precision to what, in qualita-tive terms, we know already as the result of a complete theoretical analysis-provided always that it is a case where the other considerations to be given below are satisfied.

This, of course, is absolutely right. Once you include *all* actual causes into the original (over)simple model, it may well be that the causes are no longer independent or linear, and that *a fortiori* the coefficients in the econometric equations no longer are identifiable. And so, since *all* causal factors are not included in the original econometric model, it is not an adequate representation of the real causal structure of the economy that the model is purportedly meant to represent.

Unfortunately, economists often hold the view that Keynes’s criticisms of econometrics is the conclusions of a sadly misinformed and misguided intellectual who disliked and did not understand much of it. This is really a gross misapprehension. To be careful and cautious is not the same as to dislike. And as any perusal of the mathematical statistical and philosophical works of people like for example Nancy Cartwright, Chris Chatfield, Hugo Keuzenkamp or Aris Spanos would show, the same critique is more or less put forward by respected authorities today.

I would argue, against “common knowledge”, that Keynes did not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. He knew them all too well – and was not satisfied with the validity and philosophical underpinning of the assumptions made for applying its methods. And neither was the eminent mathematical statistician David Freedman, who in his *Statistical Models and Causal Inference* wrote:

If the assumptions of a model are not derived from theory, and if predictions are not tested against reality, then deductions from the model must be quite shaky. However, without the model, the data cannot be used to answer the research question …

In my view, regression models are not a particularly good way of doing empirical work in the social sciences today, because the technique depends on knowledge that we do not have. Investigators who use the technique are not paying adequate attention to the connection – if any – between the models and the phenomena they are studying. Their conclusions may be valid for the computer code they have created, but the claims are hard to transfer from that microcosm to the larger world …

Regression models often seem to be used to compensate for problems in measurement, data collection, and study design. By the time the models are deployed, the scientific position is nearly hopeless. Reliance on models in such cases is Panglossian …

Given the limits to present knowledge, I doubt that models can be rescued by technical fixes. Arguments about the theoretical merit of regression or the asymptotic behavior of specification tests for picking one version of a model over another seem like the arguments about how to build desalination plants with cold fusion and the energy source. The concept may be admirable, the technical details may be fascinating, but thirsty people should look elsewhere …

Causal inference from observational data presents may difficulties, especially when underlying mechanisms are poorly understood. There is a natural desire to substitute intellectual capital for labor, and an equally natural preference for system and rigor over methods that seem more haphazard. These are possible explanations for the current popularity of statistical models.

Indeed, far-reaching claims have been made for the superiority of a quantitative template that depends on modeling – by those who manage to ignore the far-reaching assumptions behind the models. However, the assumptions often turn out to be unsupported by the data. If so, the rigor of advanced quantitative methods is a matter of appearance rather than substance.

*Literature*

Cartwright, Nancy (1989), *Nature’s Capacities and their Measurement*. Oxford: Clarendon Press.

Cartwright, Nancy (1999), *The Dappled World*. Cambridge: Cambridge University Press.

Cartwright, Nancy (2007), *Hunting Causes and Using Them*. Cambridge: Cambridge University Press.

Fisher, Ronald (1922), On the mathematical foundations of theoretical statistics. *Philosophical Transactions of The Royal Society A*, 222.

Freedman, David (2010), *Statistical Models and Causal Inference*. Cambridge: Cambridge University Press.

Keynes, John Maynard (1973 (1921)), *A Treatise on Probability*. Volume VIII of *The Collected Writings of John Maynard Keynes*, London: Macmillan.

Syll, Lars (2007), *John Maynard Keynes*. Stockholm: SNS Förlag.

## Recent Comments