Integrating explanation and prediction in computational social science

Jake M. Hofman (), Duncan J. Watts (), Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani and Tal Yarkoni
Additional contact information
Jake M. Hofman: Microsoft Research
Duncan J. Watts: University of Pennsylvania
Filiz Garip: Princeton University
Thomas L. Griffiths: Princeton University
Jon Kleinberg: Cornell University
Helen Margetts: University of Oxford
Matthew J. Salganik: Princeton University
Simine Vazire: University of Melbourne
Alessandro Vespignani: Northeastern University
Tal Yarkoni: University of Texas at Austin

Nature, 2021, vol. 595, issue 7866, 181-188

Abstract: Abstract Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

Date: 2021
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DOI: 10.1038/s41586-021-03659-0

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