The Data Analyst’s Guide to Cause and Effect: An Introduction to Causal Inference in Practice(Quantitative Applications in the Social Sciences)

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.

Table of Contents:
About the Authors Series Editor’s Introduction Acknowledgments Chapter 1: Introduction The fundamental promise of causal inference Causal inference is “EEESI” The R programming language Formal notation Chapter objectives Further reading Chapter 2: Causal Graphs Randomizing a DAG Elementary ingredients of DAGs Good and bad controls Where do DAGs come from? Average people and people on average Chapter objectives Further reading Chapter 3: G-methods and Marginal Effects Inverse probability weighting G-computation It’s assumptions all the way down Chapter objectives Further reading Chapter 4: Adventures in G-methods Doubly robust estimation Sub-group analysis Complex longitudinal designs Mediation analysis: Crossing hypothetical worlds Chapter objectives Further reading Chapter 5: Most of Your Data is Almost Always Missing External validity and selection bias Poststratification The treatment effects zoo Target populations and econometrics Chapter objectives Further reading Chapter 6: More Missing Data To be or not to be missing Completely random terminology Missing data imputation Chapter objectives Further reading Chapter 7: Multilevel modelling and Mundlak’s legacy Causal inference as counterfactual prediction Mundlak models Marginal effects in a multilevel model Chapter objectives Further reading Chapter 8: Causal Inference is not Easy Violations of identification assumptions and some solutions Bayesian causal modelling Perspectives on RCT data analysis Causal inference in the era of Big Data and AI Conclusion References Index

About the Author :
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

Review :

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.