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The Data Analyst’s Guide to Cause and Effect: An Introduction to Applied Causal Inference - Couverture souple

Bendixen, Theiss; Purzycki, Benjamin Grant

 
9798348848712: The Data Analyst’s Guide to Cause and Effect: An Introduction to Applied Causal Inference
Couverture souple
ISBN 13 : 9798348848712
Editeur : Sage Pubns, 2026

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
Sage Pubns
Date d'édition
2026
Langue
anglais
ISBN 13 
9798348848712
Reliure
Broché
Nombre de pages
158
Coordonnées du fabricant
non disponible
Personne responsable
non disponible