May 29th—Etienne Ollion: Machine Learning, Econometrics, and the Future of Quantification
For our last meeting of the year, the Exploring (Mixed) Methods Study Group is pleased to present:
The Great Regression: Machine Learning, Econometrics, and the Future of Quantification
Etienne Ollion, Ph.D
Visiting professor in Sociology
Tuesday, May 29th 3:30-4:50 PM
Rosenwald 329
→ RSVP Here ←
Summary
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 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 article argues that 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.
Professor Ollion will deliver a presentation, which will be followed by a discussion. An accompanying paper-in-development will be posted on the blog by the end of the week, for those cannot make it to the meeting or who would like to delve into the material in more detail.
Please let me know if you have any questions, or if I can facilitate your participation in any way.
Best,
Sanja