Causal Inference

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences
 
“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

“A new guide to methods for determining cause and effect in the social sciences. In summarising, systematising and prioritising methodological tools for researchers, this book will be of use to all social scientists looking to validate their quantitative findings.”—Simeon Mitropolitski, LSE Review of Books

“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)

“Cunningham’s brilliant book is that rare statistical treatise written for students and practitioners alike. Engaging language and vivid examples bring the tools of causal inference to a broad audience. Read the book, absorb its lessons, and you’ll develop the skills you need to credibly assess whether a statistics class, a public policy, or a new business practice truly makes a difference.”—Justin Wolfers, University of Michigan

“Accessible and engaging. An excellent introduction to the statistics of causal inference.”—Alberto Abadie, MIT

“Learning about causal effects is the main goal of most empirical research in economics. In this engaging book, Scott Cunningham provides an accessible introduction to this area, full of wisdom and wit and with detailed coding examples for practitioners.”—Guido Imbens, coauthor of Causal Inference

“This book will probably shock economics instructors with the clarity, insights, and tools that modern graphical models introduce to the teaching of econometrics. The benefits will outlast the shock.”—Judea Pearl, University of California, Los Angeles

Scott Cunningham is professor of economics at Baylor University. He is also coeditor of The Oxford Handbook of the Economics of Prostitution.

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