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Bootstrap Inference for K-Nearest Neighbour Matching Estimators

  • de Luna, Xavier

    ()

    (Umeå University)

  • Johansson, Per

    ()

    (IFAU)

  • Sjöstedt-de Luna, Sara

    ()

    (Umeå University)

Abadie 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.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 5361.

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Length: 25 pages
Date of creation: Dec 2010
Date of revision:
Handle: RePEc:iza:izadps:dp5361
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