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The Data Analyst’s Guide to Cause and Effect: An Introduction to Applied Causal Inference (Quantitative Applications in the Social Sciences, 201) - Softcover

Bendixen, Theiss; Purzycki, Benjamin Grant

 
9798348848712: The Data Analyst’s Guide to Cause and Effect: An Introduction to Applied Causal Inference (Quantitative Applications in the Social Sciences, 201)
Softcover
ISBN 13: 9798348848712
Verlag: Sage Pubns, 2026

Inhaltsangabe

Understanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst’s Guide to Cause and Effect offers a clear, practical roadmap for answering causal questions using both experimental and observational data.

Built around the EEESI workflow—Estimand, Estimator, Estimate, Simulation-based Inference—this book provides a systematic approach to defining, estimating, and validating causal effects. Readers will learn to apply modern techniques such as g-methods, inverse probability weighting, poststratification, and multilevel modeling, while tackling challenges like confounding and missing data.

With hands-on examples in R, code snippets, and simulation exercises, this guide balances rigor with accessibility. Ideal for graduate courses and applied researchers, it equips readers to move beyond simple associations and make credible causal inferences that inform theory, policy, and practice.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Sage Pubns
Erscheinungsdatum
2026
Sprache
Englisch
ISBN 13 
9798348848712
Einband
Taschenbuch
Anzahl der Seiten
158
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Verantwortliche Person
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