Machine Learning for Social Science: An Agnostic Approach

Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart

Research output: Contribution to journalReview articlepeer-review

106 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)395-419
Number of pages25
JournalAnnual Review of Political Science
StatePublished - May 11 2021

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science


  • Machine learning
  • research design
  • text as data


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