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“Do-calculus” as a book category gathers works on causal inference centered on Judea Pearl’s framework of structural causal models, directed acyclic graphs, and the do-operator. Books in this genre teach readers how to move from correlation to causation by formalizing interventions (what happens if we do X?), deriving identifiable expressions for causal effects from observational or experimental data, and designing analyses that handle confounding, selection bias, and mediation. The category spans foundational theory and algorithms (the rules of do-calculus, identification, causal discovery), practical guides to A/B testing and policy evaluation, and case-driven applications across health, economics, social science, and machine learning. These titles equip researchers and practitioners with tools like adjustment sets, instrumental variables, mediation analysis, and transportability to answer counterfactual questions and support reliable decision-making.

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