Behavioral scientists aim to explain and predict behavior. In principle, these goals align; in practice, common approaches to pursuing them have become distinct traditions in tension with one another. The explanatory tradition often examines causal factors in isolation, establishing that they have some effect but not how much or how they combine. The predictive tradition learns how factors combine, but these patterns may not reflect a causal structure or hold when conditions change. Answering how much each factor matters, and how they combine across settings, requires both predictive accuracy and causal interpretation. This article examines three developments toward this integration: evaluation frameworks that emphasize generalization, systematic experimentation and flexible models, and interpretation tools. We present recent empirical examples that demonstrate how this integration enables the discovery of generalizable patterns and provides a path toward cumulative behavioral science.
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