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dc.contributor.authorde Luna, Xavieren_US
dc.contributor.authorJohansson, Peren_US
dc.contributor.authorSjöstedt-de Luna, Saraen_US
dc.description.abstractAbadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not be applied without theoretical justification. In this paper, we present two resampling schemes, which we show provide valid inference for KNN matching estimators. We resample estimated individual causal effects (EICE), i.e. the difference in outcome between matched pairs, instead of the original data. Moreover, by taking differences in EICEs ordered with respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild assumptions on the heterogeneity are imposed. We provide proofs of the validity of the proposed resampling based inferences. A simulation study illustrates finite sample properties.en_US
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen_US
dc.relation.ispartofseries|aDiscussion paper series // Forschungsinstitut zur Zukunft der Arbeit |x5361en_US
dc.subject.keywordblock bootstrapen_US
dc.subject.keywordaverage causal/treatment effecten_US
dc.titleBootstrap inference for K-nearest neighbour matching estimatorsen_US
dc.typeWorking Paperen_US

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