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Bootstrap inference for K-nearest neighbour matching estimators

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Author Info

  • de Luna, Xavier

    ()
    (Umeå University)

  • Johansson, Per

    ()
    (IFAU - Institute for Labour Market Policy Evaluation)

  • Sjöstedt-de Luna, Sara

    ()
    (Umeå University)

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.

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Bibliographic Info

Paper provided by IFAU - Institute for Evaluation of Labour Market and Education Policy in its series Working Paper Series with number 2010:13.

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Length: 24 pages
Date of creation: 19 Nov 2010
Date of revision:
Handle: RePEc:hhs:ifauwp:2010_013

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Keywords: Block bootstrap; subsampling; average causal/treatment effect;

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Cited by:
  1. 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).
  2. 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.

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