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Structural causal models is a book category focused on the theory and practice of reasoning about cause and effect using formal models. Titles in this area explain how structural equations and graphical models (often directed acyclic graphs) encode assumptions about data-generating processes and enable causal questions to be answered, including identification, estimation, mediation, counterfactuals, and the effects of interventions. The category spans foundational texts, methodological monographs, and applied guides, often covering tools like do-calculus, causal discovery, and links to potential outcomes and structural equation modeling. Books may include worked examples, algorithms, and software workflows, with applications in economics, epidemiology, social sciences, public policy, and machine learning. Intended for researchers, data scientists, and students, these works aim to make causal reasoning explicit, testable, and actionable.

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