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.
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Theiss Bendixen is a PhD, quantitative consultant, and independent researcher. To date, he has written two popular science books, a co-edited
volume, as well as more than 40 academic works, including tutorials, quantitative empirical papers, and technical commentaries. He consults on statistical modelling in both industry, academia, and non-profits, applying causal inference techniques across scientific disciplines. He currently works in the pharmaceutical sector.
Personal website: www.theissbendixen.com
Benjamin Grant Purzycki is Associate Professor at Aarhus University. He is a cognitive and evolutionary cultural anthropologist and focuses on the causal role of various demographic and cultural factors on human cooperation. He has conducted fieldwork in the Tyva Republic (Russia) and managed large, cross-cultural projects. His most recent books include The Minds of Gods: New Horizons in the Naturalistic Study of Religion (Bloomsbury), Ethnographic Free-List Data (Sage), and Morality and the Gods (Cambridge University Press).
Personal website: www.bgpurzycki.wordpress.com
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Paperback. Condition: new. Paperback. The Data Analysts Guide to Cause and Effect offers a clear, practical roadmap built around the EEESI workflowEstimand, Estimator, Estimate, Simulation-based Inference. This book provides a systematic approach to defining, estimating, and validating causal effects, allowing readers to learn to apply modern techniques and move beyond simple associations to make credible causal inferences that inform theory, policy, and practice. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798348848712
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Paperback. Condition: New. The Data Analyst's Guide to Cause and Effect offers a clear, practical roadmap built around the EEESI workflow-Estimand, Estimator, Estimate, Simulation-based Inference. This book provides a systematic approach to defining, estimating, and validating causal effects, allowing readers to learn to apply modern techniques and move beyond simple associations to make credible causal inferences that inform theory, policy, and practice. Seller Inventory # LU-9798348848712
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Taschenbuch. Condition: Neu. Neuware -The Data Analyst¿s Guide to Cause and Effect offers a clear, practical roadmap built around the EEESI workflow-Estimand, Estimator, Estimate, Simulation-based Inference. This book provides a systematic approach to defining, estimating, and validating causal effects, allowing readers to learn to apply modern techniques and move beyond simple associations to make credible causal inferences that inform theory, policy, and practice. 168 pp. Englisch. Seller Inventory # 9798348848712
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