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Integrating explanation and prediction in computational social science

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.

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J.M.H. and D.J.W. conceptualized and helped to write and prepare the manuscript. They contributed equally to these efforts. All authors were involved in and discussed the structure of the manuscript at various stages of its development.

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Correspondence to Jake M. Hofman or Duncan J. Watts.

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Peer review information Nature thanks Noortje Marres, Melanie Mitchell and Scott Page for their contribution to the peer review of this work.

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Hofman, J.M., Watts, D.J., Athey, S. et al. Integrating explanation and prediction in computational social science. Nature 595, 181–188 (2021). https://doi.org/10.1038/s41586-021-03659-0

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