The Great Regression: Machine Learning, Econometrics, and the Future of Quantification
Etienne Ollion, Associate Professor, CNRS
Matrix is located on the 8th floor of Barrows Hall, on the UC Berkeley campus, near Telegraph and Bancroft Avenues, just up the hill from Sather Gate. There are entrances at both ends of the building, but only one of the elevators on the eastern side goes directly to the 8th floor. You can alternatively take the stairs to the 7th floor and walk up the stairs.
Abstract
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 presentation offers an introduction to this class of statistical techniques. This is done by comparing machine learning to more classic approaches to quantification – parametric regression in the first place – both at a general level and in practice. An intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science, the revolution announced by many and feared by others will not happen any time soon, at least in the terms that both proponents and critics of the technique have spelled out. Rather than ushering in a radically new scientific era, the growing use of machine learning is fostering an increased competition between the two approaches, which results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall.
About the Speaker
Etienne Ollion is Associate Professor and Permanent Researcher a the CNRS (France). He specializes in political sociology and has recently published two books (in French): Reason of State. A History of the French 'War on Cults' and Profession: MP. On the Rise of Career Politicians in France. Etienne has also a keen interest in methods, and more precisely in the type of knowledge they afford. Etienne’s research has been published in the Journal of Economic Perspectives, the European Journal of Sociology, Sociologie du Travail, and Genèses among others, and he is regularly featured in newspapers in France and abroad.
About the Social Science and Data Science Series
Starting in spring 2018, the Social Science Matrix and the D-Lab are jointly organizing a series of talks on "Social Science and Data Science." We are eager to work together with departments and other centers on campus and welcome suggestions of speakers and panels. If you would like to bring someone to our attention, please email marion.fourcade@gmail.com or ckapelke@berkeley.edu.
