A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.
Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?
Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.
By:
Paul R. Rosenbaum
Imprint: MIT Press
Country of Publication: United States
Dimensions:
Height: 178mm,
Width: 127mm,
Weight: 369g
ISBN: 9780262545198
ISBN 10: 0262545195
Series: The MIT Press Essential Knowledge series
Pages: 232
Publication Date: 04 April 2023
Audience:
General/trade
,
ELT Advanced
Format: Paperback
Publisher's Status: Active
Series Foreword ix List of Examples xi List of Methodological Topics xiii 1 The Effects Caused by Treatments 1 2 Randomized Experiments 21 3 Observational Studies: The Problem 47 4 Adjustments for Measured Covariates 67 5 Sensitivity to Unmeasured Covariates 85 6 Quasi-Experimental Devices in the Design of Observational Studies 103 7 Natural Experiments, Discontinuities, and Instruments 117 8 Replication, Resolution, and Evidence Factors 149 9 Uncertainty and Complexity in Causal Inference 159 Postscript: Key Ideas, Chapter by Chapter 175 Glossary 179 Notes 181 Bibliography 189 Further Reading 197 Index 199
Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of Observation and Experiment- An Introduction to Causal Inference, Design of Observational Studies, Observational Studies, and Replication and Evidence Factors in Observational Studies.
