Voreilige Schlussfolgerungen im Alltag: Korrelation und Kausalität

02Apr09

Ich habe auf dem Statistical Modeling, Causal Inference, and Social Science Blog einen schönen Kommentar zu Korrelation und Kausalität gefunden, von dem ich hier einige Beispiel wiedergeben werde. In jedem Statistik Kurs hört man von Scheinkorrelation und wird ermahnt, nie direkt von Korrelation auf Kausalität zu schließen. Anhand von prominenten Beispielen, wie z.B. der Korrelation zwischen der Anzahl an Störchen und der Geburtenrate einer Region, wobei diese durch den Urbanisierungsgrad (hoher Urbanisierungsgrad, d.h. städtische Region= weniger Störche und geringere Geburtenrate als auf dem Land) erklärt werden kann, wird dies schnell einsichtig. Bei realen Beispielen ist dies jedoch um einiges schwieriger zu bemerken oder gar ohne weitere Forschung nur zu erahnen und wird in der alltäglichen Argumentation somit oftmals unwissentlich und vielleicht unwillentlich genutzt.

Hier nun einige Ausschnitte aus dem umfassenden Kommentar bei denen man sich durchaus fragen könnte, „wie hätte ich die jeweilige Information als Argument genutzt?“.

(1) „Children raised by single parents do less well at school (and later in life).“ The conclusion is supposed to be that parents should become and stay married.

(2) „Children where the mother stays at home do better at school (and later in life).“ The conclusion is supposed to be that mothers should stay at home. (Or occasionally, but rarely, that mothers and fathers should stay at home, but usually this kind of survey is just a crude attack on working mothers so fathers are never mentioned.)

(3) „People who are married are happier than people who are not.“ The conclusion is supposed to be that everybody should be married (and in particular the government should force non-married people to subsidise married people via the tax system; note that normally other words are substituted for „happy“ when the tax argument is made, but it amounts to the same thing).

(6) „Men who have beards are happier than men who do not.“ The conclusion is supposed to be that all men should grow beards.

The basic situation in all the examples is that there are two groups of people, with one group having a „desirable“ property and the other group not (this is the correlation). The conclusion is supposed to be that if only people in the second group could be forced to join the first group then all would be well (this is the causation). Unfortunately there is no proof that it is membership of the group which is causing the particular observed behaviour, it could be many other things. Indeed it could even be that the causation is the other way around, so that having the desired property naturally causes someone to be in the first group, so artificially being forced into this group does no good.

It’s possible, of course, that in one or more of the cases the stated conclusion is true, but the survey (or study) does not prove this one way or the other. Causations are much harder (and more expensive) to prove than correlations.

In (1) the more significant correlation is with poverty. People who are poor have worse educations and people who are single are generally poorer (which is a reason for married, or partnered, people to pay more, not less tax). To the extent that forcing someone into marriage might raise the household income it’s possible that marriage by itself might improve the education of these children, but that would have to be offset by the obvious and serious negative consequences of such a policy.

In (2) it is obvious that in the best of all possible worlds children should be given constant care and attention by their parents. However this is not a cost-free alternative. If the mother does not work then the household will lose substantial income and that is also not good for the children (or parents). Many parents decide that working is better than not working, and parents can perhaps be trusted more than pseudo-scientists posing pseudo-surveys.

In (3) it is quite possible that happy people are very likely to get married and unhappy people not. It is not the act of marriage itself which makes people happy. The emotional state of people who are likely to get married in the first place is perhaps more settled than that of the rest. If being happy is worthy of a tax break then perhaps happy people should be given one.

In (6) we have an example almost exactly the same as (3), and nobody would treat the conclusion seriously. But no doubt on a quiet news day this kind of silly story could be given prominence in the national media.

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Filed under: Statistik   |  2 Comments
Schlagwörter: Kausalität, Korrelation