Causal Inference

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.

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.

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