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Causal inferences — what Big Data cannot give us

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Causal inferences — what Big Data cannot give us .[embedded content] The central problem with the present ‘Machine Learning’ and ‘Big Data’ hype is that so many — falsely — think that they can get away with analyzing real-world phenomena without any (commitment to) theory. But — data never speaks for itself.  Data by themselves are useless. Without a prior statistical set-up, there actually are no data at all to process. Clever data-mining tricks are not enough to answer important scientific questions. Theory matters. If we wanted highly probable claims, scientists would stick to​​ low-level observables and not seek generalizations, much less theories with high explanatory content. In this day​ of fascination with Big data’s ability to predict​ what book

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Causal inferences — what Big Data cannot give us

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The central problem with the present ‘Machine Learning’ and ‘Big Data’ hype is that so many — falsely — think that they can get away with analyzing real-world phenomena without any (commitment to) theory. But — data never speaks for itself.  Data by themselves are useless. Without a prior statistical set-up, there actually are no data at all to process.

Clever data-mining tricks are not enough to answer important scientific questions. Theory matters.

Causal inferences — what Big Data cannot give usIf we wanted highly probable claims, scientists would stick to​​ low-level observables and not seek generalizations, much less theories with high explanatory content. In this day​ of fascination with Big data’s ability to predict​ what book I’ll buy next, a healthy Popperian reminder is due: humans also want to understand and to explain. We want bold ‘improbable’ theories. I’m a little puzzled when I hear leading machine learners praise Popper, a realist, while proclaiming themselves fervid instrumentalists. That is, they hold the view that theories, rather than aiming at truth, are just instruments for organizing and predicting observable facts. It follows from the success of machine learning, Vladimir Cherkassy avers, that​ “realism is not possible.” This is very quick philosophy!

Quick indeed!

Lars Pålsson Syll
Professor at Malmö University. Primary research interest - the philosophy, history and methodology of economics.

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