Bootstrap Inference for K-Nearest Neighbour Matching Estimators
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.
|Date of creation:||Dec 2010|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +49 228 3894 223
Fax: +49 228 3894 180
Web page: http://www.iza.org
|Order Information:|| Postal: IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany|
When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp5361. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Fallak)
If references are entirely missing, you can add them using this form.