Integrating explanation and prediction in computational social science

Jake M. Hofman1, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik. Simine Vazire, Alessandro Vespignani & Tal Yarkoni.

Abstract: Computational social science is more than just large repositories of digital data an the computational methods needed to construct and analyse them. It also representsa convergence of diferent felds with diferent ways of thinking about and doingscience. The goal of this Perspective is to provide some clarity around how thes approaches difer from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The frst is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal efects, 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.