Bootstrap inference for K-nearest neighbour matching estimators
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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by IFAU - Institute for Evaluation of Labour Market and Education Policy in its series Working Paper Series with number 2010:13.
Length: 24 pages
Date of creation: 19 Nov 2010
Date of revision:
Block bootstrap; subsampling; average causal/treatment effect;
Other versions of this item:
- de Luna, Xavier & Johansson, Per & Sjöstedt-de Luna, Sara, 2010. "Bootstrap Inference for K-Nearest Neighbour Matching Estimators," IZA Discussion Papers 5361, Institute for the Study of Labor (IZA).
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- de Luna, Xavier & Johansson, Per, 2012.
"Testing for nonparametric identification of causal effects in the presence of a quasi-instrument,"
Working Paper Series
2012:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
- de Luna, Xavier & Johansson, Per, 2012. "Testing for Nonparametric Identification of Causal Effects in the Presence of a Quasi-Instrument," IZA Discussion Papers 6692, Institute for the Study of Labor (IZA).
- Juan Díaz & Tomás Rau & Jorge Rivera, 2012. "A matching estimator based on a bi-level optimization problem," Working Papers wp351, University of Chile, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Margareta Wicklander).
If references are entirely missing, you can add them using this form.