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“Causal discovery” books focus on the theory and practice of uncovering cause‑and‑effect structure from data, especially when randomized experiments are impractical. They cover foundational ideas in causal inference—structural causal models, graphical models (DAGs), counterfactuals, confounding, identifiability, and interventions—then present algorithms for learning causal graphs from observational and experimental data, such as constraint‑based (PC, FCI), score‑based (GES), hybrid methods, LiNGAM, invariant causal prediction, and approaches for time series and nonlinear settings. Typical works explain underlying assumptions (e.g., Markov, faithfulness, acyclicity, invariance), equivalence classes (CPDAGs, PAGs), statistical tests, and the use of domain knowledge, along with robustness checks, sensitivity analyses, and limits of what can be learned. Many titles include practical guidance on integrating discovery with experimentation (A/B tests), instrumental variables, and do‑calculus, plus software tooling and case studies. This category serves researchers and practitioners in machine learning, statistics, social sciences, epidemiology, economics, and the sciences who need to infer credible causal relationships to inform explanation, prediction under intervention, and decision‑making.

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