On manipulability and causation

26 Nov, 2018 at 16:08 | Posted in Statistics & Econometrics | 1 Comment

If contributions made by statisticians to the understanding of causation are to be taken over with advantage in any specific field of inquiry, then what is crucial is that the right relationship should exist between statistical and subject-matter concerns …
introduction-to-statistical-inferenceThe idea of causation as consequential manipulation is apt to research that can be undertaken primarily through experimental methods and, especially to ‘practical science’ where the central concern is indeed with ‘the consequences of performing particular acts’. The development of this idea in the context of medical and agricultural research is as understandable as the development of that of causation as robust dependence within applied econometrics. However, the extension of the manipulative approach into sociology would not appear promising, other than in rather special circumstances … The more fundamental difficulty is that​ under the — highly anthropocentric — principle of ‘no causation without manipulation’, the recognition that can be given to the action of individuals as having causal force is in fact peculiarly limited.

John H. Goldthorpe

Some statisticians and data scientists think that algorithmic formalisms somehow give them access to causality. That is, however, simply not true. Assuming ‘convenient’ things like faithfulness or stability is not to give proofs. It is to assume what has to be proven. Deductive-axiomatic methods used in statistics do no produce evidence for causal inferences. The real causality we are searching for is the one existing in the real world around us. If there is no warranted connection between axiomatically derived theorems and the real world, well, then we have not really obtained the causation we are looking for.

[Added: If you have not already read Goldthorpe’s article, you should. For every​ social scientist or economist interested in questions about causality, this modern minor classic is a must-read​.]

1 Comment

  1. Working my way thru Goldthorpe’s essay, which I agree is quite interesting.
    Quite apart from considerations of how probability and statistics apply, just defining an academic domain like “sociology” or “economics” imposes boundaries as well as methodological doctrines on what factors ought to be considered legitimate objects of inquiry. Sociologists will always like “roles” and economists, “prices” and “supply and demand”. If we discover thru biology that lead in gasoline is causing a crime wave, how do sociologists or economists make sense of that in terms of their respective discipline’s approaches to “explaining” crime? Are attempts to “refute” the lead-in-gasoline hypothesis scientifically genuine or mere disciplinary boundary-policing?
    On another front, I am not convinced by Goldthorpe that “consequential manipulation” can be so neatly isolated. Probability and statistics deals always with processes, more or less under control. A lot of confusion arises from the reluctance to admit that insufficient attention is paid to the essential context for meaning: how those processes come to be under control Knowing how to calculate expectations for the outcomes generated by a fair roulette wheel is a poor metaphor indeed, if you conveniently forget that someone had to very carefully engineer that damn roulette wheel to get such a tightly controlled process under way. A lot of consequential manipulation, by the hand of Man or Nature, goes into creating the conditions under which theorems of probability have application.

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