Working Paper Series
IFAU - Institute for Evaluation of Labour Market and Education Policy
Xavier de Luna
Bootstrap inference for K-nearest neighbour matching estimators
(), Per Johansson
() and Sara Sjöstedt-de Luna
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
Keywords: Block bootstrap; subsampling; average causal/treatment effect; (follow links to similar papers)
JEL-Codes: C14; C21; (follow links to similar papers)
24 pages, November 19, 2010
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