Causal Inference
- Length: 224 pages
- Edition: 1
- Language: English
- Publisher: The MIT Press
- Publication Date: 2023-04-04
- ISBN-10: 0262545195
- ISBN-13: 9780262545198
- Sales Rank: #110247 (See Top 100 Books)
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.
Cover Contents Series Foreword List of Examples List of Methodological Topics 1 The Effects Caused by Treatments 2 Randomized Experiments 3 Observational Studies: The Problem 4 Adjustments for Measured Covariates 5 Sensitivity to Unmeasured Covariates 6 Quasi-Experimental Devices in the Design of Observational Studies 7 Natural Experiments, Discontinuities, and Instruments 8 Replication, Resolution, and Evidence Factors 9 Uncertainty and Complexity in Causal Inference Postscript: Key Ideas, Chapter by Chapter Glossary Notes Bibliography Further Reading Index
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