Bootstrap Inference for K-Nearest Neighbour Matching Estimators

Xavier de Luna (), Per Johansson and Sara Sjöstedt-de Luna ()
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Xavier de Luna: Umeå University
Sara Sjöstedt-de Luna: Umeå University

No 5361, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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.

Keywords: block bootstrap; subsampling; average causal/treatment effect (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2010-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Bootstrap inference for K-nearest neighbour matching estimators (2010)
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