Valid Causal Inference from Observational Data

Erscheinungsjahr 2027
ISBN: 978-3-032-40937-9
Verlag: Springer

Buch, Englisch, 325 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.

The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Zielgruppe

Graduate

Autoren/Hrsg.

Cox, Jr., Louis Anthony

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Part I: Conditions and Methods for Valid Causal Inference.-  Introduction and Foundations: Asking and Answering Well-Defined Causal Questions Using Observational Data.- Conceptual and Definitional Foundations for Causal Inference.- Conceptual and Definitional Foundations for Causal Inference.- Necessary Conditions for Valid Causal Modeling and Data Analysis.- Interpretation and Integrity of Results.- Robustness, Generalizability, and External Validity.- Part II: Applications and Case Studies.- An AI Assistant for Methodological Review of Causal Conclusions About Health Risks.- An AI Assistant for Methodological Review of Causal Conclusions About Health Risks.- Index.

Über Autor(innen)

Louis Anthony Cox, Jr. is an Associate Professor of Business Analytics at the University of Colorado Denver, USA; Principal Researcher at Entanglement, Inc.; and President of Cox Associates, a Denver-based applied research company specializing in artificial intelligence and machine learning; health, safety, and environmental risk analysis; epidemiology; policy analytics; data science; and operations research. Dr. Cox is Editor-in-Chief of Risk Analysis: An International Journal. He is a member of the National Academy of Engineering, a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), and a Fellow of the Society for Risk Analysis (SRA). He has authored and co-authored over 300 journal articles and numerous books and chapters in these fields. He holds over a dozen US patents on applications of artificial intelligence, signal processing, statistics, and operations research in telecommunications. His current research interests include causal inference, artificial intelligence and machine learning, risk analysis, and advanced analytics for risk management, business, public health, and public policy applications.