The Data Analyst's Guide to Cause and Effect : An Introduction to Causal Inference in Practice - Theiss Bendixen

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The Data Analyst's Guide to Cause and Effect

An Introduction to Causal Inference in Practice

By: Theiss Bendixen, Benjamin Grant Purzycki

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Paperback | 8 May 2026

At a Glance

Paperback
168 Pages

Dimensions(cm)
21.59 x 13.97 x 0.91

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$85.80

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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.

Industry Reviews

The Data Analyst's Guide to Cause and Effect offers an excellent, comprehensive, yet accessible introduction to causal inference. With a light-hearted approach, it opens up a new perspective for those accustomed to traditional statistical analysis, shedding light on crucial aspects of data interpretation. From selecting the right controls to estimating causal effects and even tackling advanced topics like missing data and the intricacies of multilevel modeling, this book is an invaluable guide for analysts seeking to move beyond mere correlation.

Julia Rohrer University of Leipzig

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