LEADER 04229oam 2200577 450 001 9910554223803321 005 20230314214012.0 010 $a9780300255881$bebook 010 $z9780300251685$bpaper 010 $a0-300-25588-8 024 7 $a10.12987/9780300255881 035 $a(CKB)4100000011642739 035 $a(MiAaPQ)EBC6425560 035 $a(DE-B1597)570869 035 $a(DE-B1597)9780300255881 035 $a(OCoLC)1233041753 035 $a(PPN)270102736 035 $a(EXLCZ)994100000011642739 100 $a20210326h20212021 uy 0 101 0 $aeng 135 $auraz#---auuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCausal inference $ethe mixtape /$fScott Cunningham 210 1$aNew Haven, Connecticut :$cYale University Press,$d[2021] 210 4$dİ2021 215 $a1 online resource (352 pages) $cillustrations 311 0 $a0-300-25168-8 320 $aIncludes bibliographical references and index. 327 $tWhat Is Causal Inference? --$tDo Not Confuse Correlation with Causality --$tOptimizationMakes Everything Endogenous --$tExample: Identifying Price Elasticity of Demand --$tConclusion --$tProbability and Regression Review --$tDirected Acyclic Graphs --$tIntroduction --$tIntroduction to DAG Notation --$tPotential Outcomes Causal Model --$tIntroduction --$tPhysical Randomization --$tRandomization Inference --$tConclusion --$tMatching and Subclassification --$tSubclassification --$tExact Matching --$tApproximate Matching --$tRegression Discontinuity --$tHuge Popularity of Regression Discontinuity --$tEstimation Using an RDD --$tChallenges to Identification --$tReplicating a Popular Design: The Close Election --$tRegression Kink Design --$tConclusion --$tInstrumental Variables --$tHistory of Instrumental Variables: Father and Son --$tIntuition of Instrumental Variables --$tHomogeneous Treatment Effects --$tParental Methamphetamine Abuse and Foster Care --$tThe Problem of Weak Instruments --$tHeterogeneous Treatment Effects --$tApplications --$tPopular IV Designs --$tConclusion --$tPanel Data --$tDAG Example --$tEstimation --$tData Exercise: Survey of Adult Service Providers --$tConclusion --$tDifference-in-Differences --$tJohn Snow?s Cholera Hypothesis --$tEstimation --$tInference --$tProviding Evidence for Parallel Trends Through Event Studies and Parallel Leads --$tThe Importance of Placebos in DD --$tTwoway Fixed Effects with Differential Timing --$tConclusion --$tSynthetic Control --$tIntroducing the Comparative Case Study --$tPrison Construction and Black Male Incarceration --$tConclusion. 330 $aAn accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists?who generally can?t run controlled experiments to test and validate their hypotheses?apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages. 606 $aCausation 606 $aInference 606 $aanalysis of causes$9eng$2EUROVOC 606 $asocial sciences$9eng$2EUROVOC 606 $ainformation analysis$9eng$2EUROVOC 615 0$aCausation. 615 0$aInference. 615 7$aanalysis of causes. 615 7$asocial sciences. 615 7$ainformation analysis. 676 $a501 700 $aCunningham$b Scott$0813929 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554223803321 996 $aCausal inference$91818462 997 $aUNINA