Valid Causal Inference from Observational Data

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.