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Geoffrey T. Wodtke and Xiang Zhou's Causal Mediation Analysis offer a comprehensive yet accessible guide to causal mediation analysis for social scientists. They explore why an exposure affects an outcome by quantifying the processes and mechanisms through which a causal effect operates. Covering everything from traditional methods through machine learning techniques and experimental designs for analysing mediation, the authors make these methods broadly accessible through clear explanations, practical examples, and the inclusion of extensive Stata and R code, allowing readers to replicate all the empirical illustrations and apply the methods directly to their own data. Starting with methods for intuitive, simple settings, they build up to more complex analyses, ensuring a smooth learning experience. Rich in examples from across sociology, psychology, political science, and economics, the authors demonstrate the application of cutting-edge methods to real-world empirical research, providing practical tools and examples for rigorous empirical research across disciplines.
List of contents
1. Introduction; 2. Foundations of causal inference; 3. Mediation analysis with baseline confounding; 4. Mediation analysis with exposure-induced confounding; 5. Mediation analysis with multiple mediators; 6. Mediation analysis with robust estimation methods; 7. Mediation analysis with (Quasi-)experimental methods; 8. The future of causal mediation analysis.
About the author
Geoffrey T. Wodtke is a Professor in the Department of Sociology and Committee on Quantitative Methods in Social, Behavioral, and Health Research at the University of Chicago. He is the winner of the 2020 Leo Goodman Award for contributions to sociological methodology from the American Sociological Association. He earned his Ph.D. in sociology from the University of Michigan in 2014, where he also received an MA in statistics.Xiang Zhou is Professor of Sociology at Harvard University, where he is also affiliated with the Institute for Quantitative Social Science and the Harvard Data Science Initiative. He is the winner of the 2022 Leo Goodman Award for contributions to sociological methodology from the American Sociological Association. He earned his PhD in sociology and statistics from the University of Michigan in 2015.
