The Data Analyst’s Guide to Cause and Effect
SAGE Publications Inc (Verlag)
979-8-3488-4871-2 (ISBN)
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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.
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
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
| Erscheint lt. Verlag | 14.1.2027 |
|---|---|
| Reihe/Serie | Quantitative Applications in the Social Sciences |
| Verlagsort | Thousand Oaks |
| Sprache | englisch |
| Maße | 139 x 215 mm |
| Themenwelt | Sozialwissenschaften ► Politik / Verwaltung ► Politische Theorie |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| ISBN-13 | 979-8-3488-4871-2 / 9798348848712 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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