Annual Review of Political Science· 2021Q1· Review
Machine Learning for Social Science: An Agnostic Approach
- 321citations
- Q1SCImago
- 2021year
Short summary
Social scientists can leverage machine learning (ML) for concept discovery, prevalence measurement, causal assessment, and prediction, shifting towards an inductive inference approach.
AI-generated from the title and abstract; the full text is not read.
Key points
- Machine learning is essential for social scientists dealing with data abundance.
- ML applications in social science differ from computer science/statistics, focusing on discovery, measurement, causal assessment, and prediction.
- The use of ML facilitates a shift from deductive to inductive social science inference.
- An agnostic approach to ML methods, focused on social science tasks, drives progress.
AI-generated from the title and abstract; the full text is not read.
Abstract
Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions.
The authors' abstract, as published at the source. Annual Review of Political Science, 2021 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences