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The Data Analyst’s Guide to Cause and Effect
An Introduction to Applied Causal Inference
- Theiss Bendixen - Independent Researcher
- Benjamin Grant Purzycki - Aarhus University, Denmark
Volume:
201
Other Titles in:
Political Science Statistics | Quantitative/Statistical Research | Sociological Research Methods
Political Science Statistics | Quantitative/Statistical Research | Sociological Research Methods
158 pages | SAGE Publications, Inc
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.
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.
About the Authors
Series Editor’s Introduction
Acknowledgments
Chapter 1: Introduction
Chapter 2: Causal Graphs
Chapter 3: G-methods and Marginal Effects
Chapter 4: Adventures in G-methods
Chapter 5: Most of Your Data is Almost Always Missing
Chapter 6: More Missing Data
Chapter 7: Multilevel modelling and Mundlak’s legacy
Chapter 8: Causal Inference is not Easy
References
Index
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.
University of Leipzig