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Causal inference books focus on how to determine cause-and-effect relationships from data, bridging statistics, economics, epidemiology, social sciences, and machine learning. This category covers the principles and assumptions behind causal claims, including counterfactual reasoning, directed acyclic graphs (DAGs), identification strategies, and study design. Core methods include randomized experiments and their observational analogs—propensity scores, instrumental variables, difference-in-differences, regression discontinuity, mediation analysis, and natural experiments—along with guidance on dealing with confounding, selection bias, and measurement error. Titles range from accessible introductions that explain concepts and pitfalls to advanced texts offering mathematical treatments and algorithms for estimation and causal discovery. Readers learn how to design studies, analyze data, and interpret results for policy evaluation, clinical research, business decisions, and scientific explanation, with an emphasis on transparency, robustness, and clear communication of assumptions.

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