| Description |
1 online resource (x, 572 pages) : illustrations |
| Contents |
Frontmatter -- Contents -- Acknowledgments -- Introduction -- Introductory Note -- What Is Causal Inference? -- Do Not Confuse Correlation with Causality -- OptimizationMakes Everything Endogenous -- Example: Identifying Price Elasticity of Demand -- Conclusion -- Probability and Regression Review -- Directed Acyclic Graphs -- Introduction -- Introduction to DAG Notation -- Potential Outcomes Causal Model -- Introduction -- Physical Randomization -- Randomization Inference -- Conclusion -- Matching and Subclassification -- Subclassification -- Exact Matching -- Approximate Matching -- Regression Discontinuity -- Huge Popularity of Regression Discontinuity -- Estimation Using an RDD -- Challenges to Identification -- Replicating a Popular Design: The Close Election -- Regression Kink Design -- Conclusion -- Instrumental Variables -- History of Instrumental Variables: Father and Son -- Intuition of Instrumental Variables -- Homogeneous Treatment Effects -- Parental Methamphetamine Abuse and Foster Care -- The Problem of Weak Instruments -- Heterogeneous Treatment Effects -- Applications -- Popular IV Designs -- Conclusion -- Panel Data -- DAG Example -- Estimation -- Data Exercise: Survey of Adult Service Providers -- Conclusion -- Difference-in-Differences -- John Snow's Cholera Hypothesis -- Estimation -- Inference -- Providing Evidence for Parallel Trends Through Event Studies and Parallel Leads -- The Importance of Placebos in DD -- Twoway Fixed Effects with Differential Timing -- Conclusion -- Synthetic Control -- Introducing the Comparative Case Study -- Prison Construction and Black Male Incarceration -- Conclusion -- Bibliography -- Permissions -- Index |
| Summary |
"An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists--who generally can't run controlled experiments to test and validate their hypotheses--apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages."--Publisher's description |
| Bibliography |
Includes bibliographical references (pages 541-553) and index |
| Notes |
Description based on print version record; title from digital title page (viewed on November 22, 2023) |
| Subject |
Causation.
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Inference.
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Dependence (Statistics)
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Social sciences -- Methodology.
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Social sciences -- Data processing.
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BUSINESS & ECONOMICS -- Econometrics.
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Social sciences -- Methodology
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Social sciences -- Data processing
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Dependence (Statistics)
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Causation
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Inference
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| Form |
Electronic book
|
| ISBN |
9780300255881 |
|
0300255888 |
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