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Title:Bootstrap inference for K-nearest neighbour matching estimators PDF Logo
Authors:de Luna, Xavier
Johansson, Per
Sjöstedt-de Luna, Sara
Issue Date:2010
Series/Report no.:Working paper // IFAU - Institute for Labour Market Policy Evaluation 2010:13
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.
Subjects:block bootstrap
subsampling
average causal/ treatment effect
Document Type:Working Paper
Appears in Collections:Working Papers, IFAU - Institute for Evaluation of Labour Market and Education Policy

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