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Causal Inference
About this book
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.
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Information
Table of contents
- Cover
- Contents
- Series Foreword
- List of Examples
- List of Methodological Topics
- 1âThe Effects Caused by Treatments
- 2âRandomized Experiments
- 3âObservational Studies: The Problem
- 4âAdjustments for Measured Covariates
- 5âSensitivity to Unmeasured Covariates
- 6âQuasi-Experimental Devices in the Design of Observational Studies
- 7âNatural Experiments, Discontinuities, and Instruments
- 8âReplication, Resolution, and Evidence Factors
- 9âUncertainty and Complexity in Causal Inference
- Postscript: Key Ideas, Chapter by Chapter
- Glossary
- Notes
- Bibliography
- Further Reading
- Index
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