Observation and Experiment

An Introduction to Causal Inference

Paul Rosenbaum

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English

Harvard University Press
13 August 2019

Medical research; Epidemiology & medical statistics; Medical sociology; Scientific equipment, experiments & techniques; Impact of science & technology on society

In the daily news and the scientific literature, we are faced with conflicting claims about the effects caused by some treatments, behaviors, and policies. A daily glass of wine prolongs life, or so we are told. Yet we are also told that alcohol can cause life-threatening cancer and that pregnant women should abstain from drinking. Some say that raising the minimum wage decreases inequality while others say it increases unemployment. Investigators once confidently claimed that hormone replacement therapy reduces the risk of heart disease but today investigators confidently claim it raises that risk. How should we study such questions?

Observation and Experiment is an introduction to causal inference from one of the field's leading scholars. Using minimal mathematics and statistics, Paul Rosenbaum explains key concepts and methods through scientific examples that make complex ideas concrete and abstract principles accessible.

Some causal questions can be studied in randomized trials in which coin flips assign individuals to treatments. But because randomized trials are not always practical or ethical, many causal questions are investigated in nonrandomized observational studies. To illustrate, Rosenbaum draws examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry. Readers gain an understanding of the design and interpretation of randomized trials, the ways they differ from observational studies, and the techniques used to remove, investigate, and appraise bias in observational studies. Observation and Experiment is a valuable resource for anyone with a serious interest in the empirical study of human health, behavior, and well-being.

By: Paul Rosenbaum
Imprint: Harvard University Press
Country of Publication: United States
Dimensions: Height: 235mm, Width: 156mm,
ISBN: 9780674241633
ISBN 10: 0674241630
Pages: 400
Publication Date: 13 August 2019
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Paul R. Rosenbaum is Robert G. Putzel Professor of Statistics at the Wharton School and a Senior Fellow of the Leonard Davis Institute of Health Economics, University of Pennsylvania.

Reviews for Observation and Experiment: An Introduction to Causal Inference

A treasure trove of considerations and strategies for making causal inferences from observational studies and experiments. The book is a joy to read and contains interesting material for readers at all levels of experience with causal inference.-- (08/01/2017) A well-written and thoughtful reflection on the doing of causal inference from one of causal inference's noted experts.--Jameson A. Quinn and Luke W. Miratrix Journal of the American Statistical Association (10/01/2018) Rosenbaum is a gifted expositor, and as a result, this book is an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.--Joel B. Greenhouse and Edward H. Kennedy Psychometrika (08/01/2018) The author's voice is an important element in the book's success. Rosenbaum is consistently clear and direct, and seems at times to be speaking directly to the reader. His excellent set of examples (twenty-five of them altogether) bring the more theoretical discussions to life.-- (08/14/2017) The book is a very valuable contribution... Highly recommended.--Carol Joyce Blumberg International Statistical Review (03/01/2018) Rosenbaum's book is, as would be expected, a carefully and precisely written treatment of its subject, reflecting superb statistical understanding, all communicated with the skill of a master teacher.--Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom A researcher seeking instruction in the sophisticated use of [statistical significance] techniques may want to consult Observation and Experiment.--James Ryerson New York Times Book Review (02/16/2018)