Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyze them. It also represents a convergence of different fields with different ways of thinking about and doing science. In this talk, I discuss how these approaches differ from one another and propose how they might be more productively integrated. First, I propose 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. Second, I advocate that computational social scientists devote more attention to combining prediction and explanation, which I call “integrative modeling,” and describe a concrete case study involving cooperation in public good games to illustrate how integrative modeling can be implemented.