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Demystifying Causal Inference [electronic resource] : Public Policy Applications with R / by Vikram Dayal, Anand Murugesan

Author
Dayal, Vikram
Published
Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edition
1st ed. 2023.
Physical Description
XV, 294 p. 125 illus., 85 illus. in color. online resource
Additional Creators
Murugesan, Anand and SpringerLink (Online service)
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Contents
John Snow and causal inference -- RStudio and R -- Regression and simulation -- Potential outcomes -- Causal graphs -- Experiments -- Matching -- Instrumental Variables -- Regression Discontinuity Design -- Panel Data and fixed effects -- Difference-in-Differences -- Integrating and generalizing causal estimates.
Summary
This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow's heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.
Subject(s)
ISBN
9789819939053
Digital File Characteristics
text file PDF
Part Of
Springer Nature eBook
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