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“Causal graphs” is a category for books that explain how to represent and reason about cause-and-effect using nodes and arrows—most often directed acyclic graphs (DAGs) and related formalisms like structural causal models and Bayesian networks. These books cover the foundations of causal inference (identification, confounding, mediation, selection bias, counterfactuals), methods for designing studies and estimating effects, and algorithms for learning causal structure from data. Many titles blend theory with practical guidance, offering tutorials, checklists, and case studies across fields such as epidemiology, economics, social science, biomedicine, and machine learning. Geared to researchers, data scientists, and practitioners, the category spans introductory texts through advanced monographs that formalize causal questions and support transparent, testable reasoning about interventions.

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