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“Model specification” as a book category encompasses works on how to design, select, and validate formal models that represent real-world systems and data. These books show how to translate theory and questions into model structure—choosing variables and features, functional forms, distributions, constraints, and assumptions—while ensuring identifiability, interpretability, and alignment with evidence. Typical topics include estimation and model selection criteria (e.g., likelihood methods, cross-validation, AIC/BIC), diagnostic testing and misspecification checks, managing complexity (overfitting, multicollinearity), causal identification and confounding, and robustness and sensitivity analysis. Spanning statistics, econometrics, machine learning, and domain modeling across the social and natural sciences and engineering, this category includes methodological texts and applied case studies that guide readers from problem framing through to a defensible, deployable model.

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