Title :

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

Author(s) :

Boelaert, Julien [Auteur]
Ollion, Étienne [Auteur]

Journal title :

Revue française de sociologie

Abbreviated title :

RFS

Volume number :

2018/3

Pages :

475-506

Publisher :

Presses de Sciences Po / Centre National de la Recherche Scientifique

Publication date :

2018

ISSN :

0035-2969

HAL domain(s) :

Sciences de l'Homme et Société/Sociologie

English abstract : [en]

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 ...
Show more >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 \u2013 most notably parametric regression\u2013 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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

20.500.12210/2

Collections :

• Centre d'Études et de Recherches Administratives, Politiques et Sociales (CERAPS) - UMR 8026

Submission date :

2024-10-02T10:29:47Z