Machine Learning for Sociology
Open accessAbstract
Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition to serving similar purposes in sociology, machine learning tools can speak to long-standing questions on the limitations of the linear modeling framework, the criteria for evaluating empirical findings, transparency around the context of discovery, and the epistemological core of the discipline.
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Predictive models tend to be 'black boxes' that do not contribute to a deeper understanding of social phenomena at the level of the explanation.
“As a consequence, predictive models tend to be 'black boxes' that do not contribute to a deeper understanding of social phenomena at the level of the explanation (Molina and Garip 2019).”
From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Molina and Garip 2019
The “Classical Statistics Versus Machine Learning” passage says predictive modeling can produce black-box results that offer little insight into mechanisms. But the “Predictions for Policy and Theory Development” section also describes predictions as a starting point for new insights and theory, so the claim overstates the paper by saying predictive models do not contribute to explanation. (LLM, conf=0.88)
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- Authors
- Molina, M., & Garip, F.
- Affiliations
- Cornell University (US)
- Corresponding author
- Garip, Filiz
- Venue
- Annual Review of Sociology
- Publisher
- Annual Reviews
- Published
- 2019-07-30
- Volume
- 45
- Issue
- 1
- Pages
- 27-45
- ISSN
- 0360-0572 (ISSN-L)
- 1545-2115
- OpenAlex
- W2946322287
- Cited by
- 295
- References
- 121
- Citations by year
- 2026: 36 · 2025: 30 · 2024: 45 · 2023: 69 · 2022: 45 · 2021: 50 · 2020: 15 · 2019: 5
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Sociology (publishedVersion)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- machine learning, population heterogeneity, causal inference, context of discovery, epistemology of sociology
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