001481480 000__ 03984cam\\22005537i\4500
001481480 001__ 1481480
001481480 003__ OCoLC
001481480 005__ 20231031003339.0
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001481480 019__ $$a1401648451$$a1401907614$$a1402032682
001481480 020__ $$a9789819939053$$q(electronic bk.)
001481480 020__ $$a9819939054$$q(electronic bk.)
001481480 020__ $$z9819939046
001481480 020__ $$z9789819939046
001481480 0247_ $$a10.1007/978-981-99-3905-3$$2doi
001481480 035__ $$aSP(OCoLC)1404263451
001481480 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLKB
001481480 049__ $$aISEA
001481480 050_4 $$aJF1525.P6
001481480 08204 $$a320.60727$$223/eng/20231017
001481480 1001_ $$aDayal, Vikram,$$eauthor.
001481480 24510 $$aDemystifying causal inference :$$bpublic policy applications with R /$$cVikram Dayal, Anand Murugesan.
001481480 264_1 $$aSingapore :$$bSpringer,$$c2023.
001481480 300__ $$a1 online resource (386 pages) :$$billustrations (black and white, and color).
001481480 336__ $$atext$$btxt$$2rdacontent
001481480 337__ $$acomputer$$bc$$2rdamedia
001481480 338__ $$aonline resource$$bcr$$2rdacarrier
001481480 504__ $$aIncludes bibliographical references.
001481480 5050_ $$aJohn Snow and causal inference -- RStudio and R -- Regression and simulation -- Potential outcomes -- Causal graphs -- Experiments -- Matching -- Instrumental Variables -- Regression Discontinuity Design -- Panel Data and fixed effects -- Difference-in-Differences -- Integrating and generalizing causal estimates.
001481480 506__ $$aAccess limited to authorized users.
001481480 520__ $$aThis book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specic causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snows heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.
001481480 588__ $$aDescription based on print version record.
001481480 650_0 $$aPolitical planning$$xData processing.$$zUnited States$$0(DLC)sh2008109397
001481480 650_0 $$aPolitical planning$$xStatistical methods.$$zUnited States$$0(DLC)sh2008109397
001481480 650_0 $$aCausation$$xStatistical methods.
001481480 650_0 $$aR (Computer program language)$$xStatistical methods.$$0(DLC)sh2002004407
001481480 655_0 $$aElectronic books.
001481480 7001_ $$aMurugesan, Anand,$$eauthor.
001481480 77608 $$iPrint version:$$aDAYAL, VIKRAM. MURUGESAN, ANAND.$$tDEMYSTIFYING CAUSAL INFERENCE.$$d[Place of publication not identified] : SPRINGER VERLAG, SINGAPOR, 2023$$z9819939046$$w(OCoLC)1381183690
001481480 852__ $$bebk
001481480 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3905-3$$zOnline Access$$91397441.1
001481480 909CO $$ooai:library.usi.edu:1481480$$pGLOBAL_SET
001481480 980__ $$aBIB
001481480 980__ $$aEBOOK
001481480 982__ $$aEbook
001481480 983__ $$aOnline
001481480 994__ $$a92$$bISE