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Demystifying causal inference = public policy applications with R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Demystifying causal inference/ by Vikram Dayal, Anand Murugesan.
Reminder of title:	public policy applications with R /
Author:	Dayal, Vikram.
other author:	Murugesan, Anand.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xv, 294 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Political planning - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-99-3905-3
ISBN:	9789819939053

Demystifying causal inference = public policy applications with R /
Dayal, Vikram.

Demystifying causal inferencepublic policy applications with R /[electronic resource] :by Vikram Dayal, Anand Murugesan. - Singapore :Springer Nature Singapore :2023. - xv, 294 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789819939053

Standard No.: 10.1007/978-981-99-3905-3doiSubjects--Topical Terms:

1430335
Political planning
--Data processing.

LC Class. No.: JF1525.P6

Dewey Class. No.: 320.60727

Demystifying causal inference = public policy applications with R /
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