Home > Uncategorized > Why the idea of causation cannot be a purely statistical one

Why the idea of causation cannot be a purely statistical one

from Lars Syll

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-inferenceWhere the ultimate aim of research is not prediction per se but rather causal explanation, an idea of causation that is expressed in terms of predictive power — as, for example, ‘Granger’ causation — is likely to be found wanting. Causal explanations cannot be arrived at through statistical methodology alone: a subject-matter input is also required in the form of background knowledge and, crucially, theory …

Likewise, the 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

Causality in social sciences — and economics — can never solely be a question of statistical inference. Statistics and data often serve to suggest causal accounts, but causality entails more than predictability, and to really in depth explain social phenomena require theory. Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. First, when we are able to tie actions, processes or structures to the statistical relations detected, can we say that we are getting at relevant explanations of causation.

5cd674ec7348d0620e102a79a71f0063Most facts have many different, possible, alternative explanations, but we want to find the best of all contrastive (since all real explanation takes place relative to a set of alternatives) explanations. So which is the best explanation? Many scientists, influenced by statistical reasoning, think that the likeliest explanation is the best explanation. But the likelihood of x is not in itself a strong argument for thinking it explains y. I would rather argue that what makes one explanation better than another are things like aiming for and finding powerful, deep, causal, features and mechanisms that we have warranted and justified reasons to believe in. Statistical — especially the variety based on a Bayesian epistemology — reasoning generally has no room for these kinds of explanatory considerations. The only thing that matters is the probabilistic relation between evidence and hypothesis. That is also one of the main reasons I find abduction — inference to the best explanation — a better description and account of what constitute actual scientific reasoning and inferences.

In the social sciences … regression is used to discover relationships or to disentangle cause and effect. However, investigators have only vague ideas as to the relevant variables and their causal order; functional forms are chosen on the basis of convenience or familiarity; serious problems of measurement are often encountered.

Regression may offer useful ways of summarizing the data and making predictions. Investigators may be able to use summaries and predictions to draw substantive conclusions. However, I see no cases in which regression equations, let alone the more complex methods, have succeeded as engines for discovering causal relationships.

David Freedman

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’s 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 haven’t really obtained the causation we are looking for.

  1. June 25, 2021 at 4:16 am

    Does anyone in these discussions ever consider the idea of a hypothesis?

    Science does not prove causality or anything else. It poses hypotheses and then evaluates how well their implications correspond with observations. If they correspond reasonably well they may be *useful* descriptions of what we can observe, within a certain context.

  2. Gerald Holtham
    June 25, 2021 at 11:07 pm

    Indeed Geoff, I agree entirely. We have blogs repeatedly flogging the straw man, pointing out that correlation cannot prove causality – as if anyone sane ever claimed it could. You start with a hypothesis, which may come from familiarity with data or may have been dreamed up as an exercise in pure imagination. The question then:: is the hypothesis consistent with data? If it is testable it will imply correlations among variables. Statistical analysis will tell you whether they are present. If they are not, there is something wrong with the hypothesis. Sometimes statistical tests have low power and sometime the hypothesis cannot be tested without auxiliary hypotheses so falsification becomes ambiguous. There is no magic truth machine but if we do not attempt empirical testing there is no hope of progress,

  3. pfeffertag
    June 29, 2021 at 5:41 am

    “…is the hypothesis consistent with data? If it is testable it will imply correlations among variables. Statistical analysis will tell you whether they are present.”

    I don’t think so. Economists and other social scientists are used to using statistics but for theory testing it is a mighty mistake. Empirical testing is vital but statistics is not the way to test a science theory except at the atomic level. Above the atomic level (or at least above the molecular level, thinking of thermodynamics) statistics does not test a science theory and so cannot falsify it.

    Now that is a clear claim. It can be refuted by producing a counter-example. Just name a science theory that was tested statistically.

    Social science has been trying to test its “theories” statistically since the 1930s. Nowadays, maybe a million papers a year purport to do this. The result is nothing. The scientific progress is nil. (That is another clear claim.)

    Empirical testing of theory is fundamental. But statistics is not how science does it and it won’t work.

  4. Craig
    June 29, 2021 at 8:23 am

    The true test of any theory is does it resolve current problems. That, and does it enable the resolution of problems not even originally considered.

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