國立高雄大學圖資館 |

登入

回首頁

Demystifying causal inferencepublic policy applications with R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Demystifying causal inferenceby Vikram Dayal, Anand Murugesan.
其他題名:	public policy applications with R /
作者:	Dayal, Vikram.
其他作者:	Murugesan, Anand.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xv, 294 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Political planningData processing.
電子資源:	https://doi.org/10.1007/978-981-99-3905-3
ISBN:	9789819939053$q(electronic bk.)

Demystifying causal inferencepublic 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$q(electronic bk.)

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

894455
Political planning
--Data processing.

LC Class. No.: JF1525.P6

Dewey Class. No.: 320.60727

Demystifying causal inferencepublic policy applications with R /
LDR:02788nmm a2200325 a 4500 001 651292
003 DE-He213
005 20230929094401.0
006 m d
007 cr nn 008maaau
008 240327s2023 si s 0 eng d
020 $a 9789819939053$q(electronic bk.)
020 $a 9789819939046$q(paper)
024 7 $a 10.1007/978-981-99-3905-3 $2 doi
035 $a 978-981-99-3905-3
040 $a GP $c GP
041 0 $a eng
050 4 $a JF1525.P6
072 7 $a KCA $2 bicssc
072 7 $a BUS069030 $2 bisacsh
072 7 $a KCA $2 thema
082 0 4 $a 320.60727 $2 23
090 $a JF1525.P6 $b D275 2023
100 1 $a Dayal, Vikram. $3 676831
245 1 0 $a Demystifying causal inference $h [electronic resource] : $b public policy applications with R / $c by Vikram Dayal, Anand Murugesan.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xv, 294 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Political planning $x Data processing. $2 fast $3 894455
650 0 $a Political planning $x Statistical methods. $3 692865
650 0 $a Causation $x Statistical methods. $3 961461
650 0 $a R (Computer program language) $x Statistical methods. $3 544756
650 1 4 $a Quantitative Economics. $3 913171
650 2 4 $a Public Policy. $3 224661
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 913173
700 1 $a Murugesan, Anand. $3 961460
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-3905-3
950 $a Economics and Finance (SpringerNature-41170)