Social scientists distinguish between predictive and causal research. While this distinction clarifies the aims of two research traditions, this clarity is being blurred by the introduction of machine learning (ML) algorithms. Although ML excels in prediction, scholars are increasingly using ML not only for prediction but also for causation. While using ML for causation appears as a category mistake, this article shows that there is a third kind of research problem in which causal and predictive inference form an intricate synergy. This synergy arises from a specific type of statistical practice, guided by what we propose, the hybrid modeling culture (HMC). Navigating through a parallel debate in the statistical sciences, this article identifies key characteristics of HMC, thereby fueling the evolution of statistical cultures in the social sciences towards better practices—meaning increasingly reliable, valid, and replicable causal inference. A corollary of HMC is that the distinction between prediction and causation, taken to its limit, melts away.
