أعرض تسجيلة المادة بشكل مبسط

dc.creator Becker, Sascha O.
dc.creator Caliendo, Marco
dc.date 2007
dc.date.accessioned 2013-10-16T06:59:44Z
dc.date.available 2013-10-16T06:59:44Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/18391
dc.identifier ppn:523979053
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/18391
dc.description Matching has become a popular approach to estimate average treatment effects. It is based on the conditional independence or unconfoundedness assumption. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature. If there are unobserved variables which affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise to which matching estimators are not robust. We address this problem with the bounding approach proposed by Rosenbaum (2002), where mhbounds allows the researcher to determine how strongly an unmeasured variable must influence the selection process in order to undermine the implications of the matching analysis.
dc.language eng
dc.publisher Deutsches Institut für Wirtschaftsforschung (DIW) Berlin
dc.relation DIW-Diskussionspapiere 659
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject ddc:330
dc.subject matching
dc.subject treatment effects
dc.subject sensitivity analysis
dc.subject unobserved heterogeneity
dc.subject Schätztheorie
dc.subject Sensitivitätsanalyse
dc.subject Theorie
dc.subject Statistical matching
dc.title mhbounds: Sensitivity Analysis for Average Treatment Effects
dc.type doc-type:workingPaper


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أعرض تسجيلة المادة بشكل مبسط