Demystifying causal inference : public policy applications with R / Vikram Dayal, Anand Murugesan.
Dayal, Vikram, author.; Murugesan, Anand, author.
2023
JF1525.P6
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Demystifying causal inference : public policy applications with R / Vikram Dayal, Anand Murugesan.
Author Dayal, Vikram, author.
ISBN 9789819939053 (electronic bk.)
9819939054 (electronic bk.)
9819939046
9789819939046
DOI https://doi.org/10.1007/978-981-99-3905-3
Published Singapore : Springer, 2023.
Language English
Description 1 online resource (386 pages) : illustrations (black and white, and color).
Other Standard Identifiers 10.1007/978-981-99-3905-3 doi
Call Number JF1525.P6
Dewey Decimal Classification 320.60727
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 specic 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 Snows 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Murugesan, Anand, author.
Available in Other Form DEMYSTIFYING CAUSAL INFERENCE.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export