Demystifying Causal Inference : Public Policy Applications with R / / Vikram Dayal and Anand Murugesan

• Autore: Dayal Vikram
• Pubblicazione: Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (304 pages)
• Disciplina: 320.6
• Soggetto topico: Political planning - Data processing; Political planning - Statistical methods; R (Computer program language) - Statistical methods
• Persona (resp. second.): MurugesanAnand
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Acknowledgements -- Contents -- About the Authors -- 1 John Snow and Causal Inference -- 1.1 Grey Skies and Cholera Deaths in London -- 1.2 Curious Dr. Snow's Early Investigations -- 1.2.1 Unpacking the Mode of Communication of Cholera -- 1.2.2 Snow's Clinical Work on the Pathology of Cholera -- 1.2.3 Mechanisms and Conditions of Cholera Transmission -- 1.3 Cholera, a Waterborne Disease -- 1.4 The Interesting but Inconclusive Broad Street Pump Incident -- 1.5 The Grand Experiment in London -- 1.5.1 Snow's Shoe-Leather Work for Identifying the Causal Links -- 1.6 Snow's Quasi-experimental Design -- 1.6.1 Treatment Intensity and Comparative Cases -- 1.7 Archimedean Lever: Instrumental Variables -- 1.8 Potential Outcomes -- 1.9 The Chapters Ahead -- 2 RStudio and R -- 2.1 Introduction -- 2.2 RStudio -- 2.3 Use Projects and a Script -- 2.4 Typical R Code -- 2.4.1 Making a Vector -- 2.4.2 Installing and Loading Packages -- 2.4.3 Data -- 2.4.4 Graphs -- 2.4.5 Regression -- 2.5 Bare Bones Example of Working with R -- 2.6 Resources -- 2.6.1 For Better Understanding -- 2.6.2 For Exploring Further -- 3 Regression and Simulation -- 3.1 Introduction -- 3.2 Sampling Distribution and Simulation -- 3.3 Mean and Regression -- 3.3.1 Estimating the Mean is the Same as Regressing on a Constant -- 3.3.2 Sampling Distribution of the Mean -- 3.4 Bivariate Regression -- 3.4.1 Bivariate Regression and Conditional Means -- 3.4.2 Sampling Distribution of the Regression Coefficient in a Bivariate Regression -- 3.4.3 Estimating a Difference is the Same as Regressing on an Indicator Variable -- 3.5 The P Value Function: A Tool for Inference -- 3.6 Systematic and Random Error -- 3.7 Resources -- 3.7.1 For Better Understanding -- 3.7.2 For Going Further -- 4 Potential Outcomes -- 4.1 Introduction -- 4.2 Basic Ideas -- 4.3 Basic Identity of Causal Inference. ; 4.4 Rubin Doctor Example -- 4.5 Assumptions for Causal Inference Using Potential Outcomes -- 4.6 Manski Bounds: Recidivism -- 4.7 R Code (Corresponding to Section 4.4) -- 4.8 Resources -- 4.8.1 For Better Understanding -- 4.8.2 For Going Further -- 5 Causal Graphs -- 5.1 Introduction -- 5.2 Concepts and Examples -- 5.2.1 Causal Graphs for Two Variables -- 5.2.2 Causal Graphs for Three Variables -- 5.2.3 Causal Graphs with ggdag Package -- 5.2.4 Assumptions for Causal Inference Using Causal Graphs -- 5.2.5 Electoral Systems -- 5.2.6 Collider Bias in Public Health -- 5.3 R code -- 5.3.1 Causal Graphs for two Variables (Corresponding to Section 5.2.1) -- 5.3.2 Causal Graphs for Three Variables (Corresponding to section 5.2.2) -- 5.3.3 ggadag use (Corresponding to section 5.2.3) -- 5.3.4 Electoral Systems (Corresponding to section 5.2.5) -- 5.4 Resources -- 5.4.1 For Better Understanding -- 5.4.2 For Going Further -- 6 Experiments -- 6.1 Introduction -- 6.2 Examples and Concepts -- 6.2.1 Anchoring Affects Judgments -- 6.2.2 Women as Policymakers -- 6.2.3 Small Class Size and Student Learning Outcomes -- 6.2.4 Simulate to Understand -- 6.3 R Code -- 6.3.1 Anchoring Affects Judgments -- 6.3.2 Women as Policymakers -- 6.3.3 Small Class Size and Student Learning Outcomes -- 6.3.4 Simulate to Understand -- 6.4 Resources -- 6.4.1 For Better Understanding -- 6.4.2 For Going Further -- 7 Matching -- 7.1 Introduction -- 7.2 Concepts and Examples -- 7.2.1 Lalonde's Study -- 7.2.2 Simple Numerical Example -- 7.2.3 Generating Apples to Apples -- 7.2.4 Exact Matching -- 7.2.5 Coarsened Exact Matching -- 7.2.6 Decentralized Forest Management -- 7.2.7 Propensity Score Matching -- 7.2.8 Mahalanobis Distance Matching -- 7.2.9 Genetic Matching -- 7.2.10 Model Dependence and Cherry-Picking -- 7.3 R Code -- 7.3.1 Lalonde's Data -- 7.3.2 Decentralized Forest Management. ; 7.3.3 Your Turn, Compensation for Injury -- 7.4 Resources -- 7.4.1 For Better Understanding -- 7.4.2 For Exploring Further -- 8 Instrumental Variables -- 8.1 Introduction -- 8.2 Concepts and Examples -- 8.2.1 A Basic Example with an Encouragement Design -- 8.2.2 Prologue to Leveraging Instrumental Variables -- 8.2.3 Colonial Origins of Economic Development -- 8.2.4 Globalization, Voter Preferences, and Brexit -- 8.2.5 Wrights' Lever Solves the `Chicken and Egg Problem' -- 8.2.6 Taxes and Consumption of Cigarettes -- 8.2.7 Overidentification Test is only Indicative of IV Validity -- 8.3 R Code -- 8.3.1 A Basic Example with an Encouragement Design -- 8.3.2 Colonial Origins of Economic Development -- 8.3.3 Globalization, Voter Preferences, and Brexit -- 8.3.4 Taxes on Consumption of Cigarettes -- 8.3.5 Overidentification is Only Indicative of IV Validity -- 8.4 Resources -- 8.4.1 For Better Understanding -- 8.4.2 For Going Further -- 9 Regression Discontinuity Design -- 9.1 Introduction -- 9.2 Concepts and Examples -- 9.2.1 Minimum Legal Drinking Age and Fatalities in the US -- 9.2.2 Term Limits and Politician Performance in Brazil -- 9.2.3 Rural Roads and Economic Development in India -- 9.3 R Code -- 9.3.1 Minimum Legal Drinking Age and Fatalities in the US -- 9.3.2 Term Limits and Politician Performance in Brazil -- 9.3.3 Rural Roads and Economic Development in India -- 9.3.4 Simple Example with Simulation -- 9.4 Resources -- 9.4.1 For Better Understanding -- 9.4.2 For Going Further -- 10 Panel Data and Fixed Effects -- 10.1 Introduction -- 10.2 Concepts and Examples -- 10.2.1 Schooling and Wages -- 10.2.2 Alcohol Policies and Traffic Fatalities -- 10.2.3 Causal Graphs for Panel Data -- 10.2.4 Income and Democracy -- 10.3 R Code -- 10.3.1 Schooling and Wages -- 10.3.2 Alcohol Policies and Traffic Fatalities -- 10.3.3 Causal Graphs for Panel Data. ; 10.3.4 Income and Democracy -- 10.4 Resources -- 10.4.1 For Better Understanding -- 10.4.2 For Going Further -- 11 Difference-in-Differences -- 11.1 Introduction -- 11.2 Concepts and Examples -- 11.2.1 Worker Injury Benefits and Time Out of Work -- 11.2.2 Great Depression Policies to Avoid Banking Collapse -- 11.2.3 Snow's Prototype DID Design -- 11.2.4 Assumptions and DID Validity -- 11.2.5 Informative Bounds on the Effect of Right-to-Carry Gun Laws -- 11.2.6 The Synthetic Control Method -- 11.3 R Code -- 11.3.1 Worker Injury Benefits and Time Out of Work -- 11.3.2 Great Depression Policies to Avoid Banking Collapse -- 11.3.3 Informative Bounds on the Effect of Right-to-Carry Gun Laws -- 11.3.4 Economic Costs of German Reunification -- 11.4 Resources -- 11.4.1 For Understanding Better -- 11.4.2 For Going Further -- 12 Integrating and Generalizing Causal Estimates -- 12.1 Introduction -- 12.2 Concepts and Examples -- 12.2.1 Statistical Approach -- 12.2.2 Analyses Guided by the Potential Outcomes Framework -- 12.2.3 Analyses Guided by Causal Graphs -- 12.3 R Code -- 12.3.1 Statistical Approach -- 12.3.2 Analyses Guided by the Potential Outcomes Framework -- 12.3.3 Analyses Guided by Causal Graphs -- 12.4 Resources -- 12.4.1 For Better Understanding -- 12.4.2 For Going Further.
• Titolo autorizzato: Demystifying Causal Inference
• ISBN: 981-9939-05-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910746977403321
• Lo trovi qui: Univ. Federico II