Abstract
There is a broad agreement that some of the most relevant problems in the social and behavioral sciences are fundamentally structural, and as a consequence require structural explanations. Yet researchers disagree on what a structural explanation is, and what are the specific questions that can only be answered through a structural lens. In this study, I shed some light on the nature of structural explanations by distinguishing between three types of structural questions related to structural proximity, structural cohesion and structural importance. In addition, I show how graphical methods can be used to answer these questions. In particular, I argue that structure learning algorithms can help us gain some understanding regarding causal structures, and network science can help us understand the organization of these structures. I provide an empirical application of these methods using a nationally representative dataset with a wide range of factors related to child development.
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