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The Great Regression. Machine Learning, Econometrics, and the Future of Quantitative Social Sciences

Résumé : What can machine learning do for (social) scientific analysis, and what can it do to it? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification – most notably parametric regression– both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall.
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Contributor : Etienne Ollion <>
Submitted on : Tuesday, July 17, 2018 - 11:58:11 AM
Last modification on : Monday, September 14, 2020 - 2:52:04 PM
Long-term archiving on: : Thursday, October 18, 2018 - 1:48:41 PM


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  • HAL Id : hal-01841413, version 1



Julien Boelaert, Etienne Ollion. The Great Regression. Machine Learning, Econometrics, and the Future of Quantitative Social Sciences. Revue française de sociologie, Presse de Sciences Po / Centre National de la Recherche Scientifique, 2018, 2018/3 (59), pp.475-506. ⟨hal-01841413⟩



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