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Matching Methods in Practice: Three Examples

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  • Imbens, Guido W.

    ()
    (Stanford University)

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    Abstract

    There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical practice where many researchers continue to use simple methods such as ordinary least squares regression even in settings where those methods do not have attractive properties. In this paper I discuss some of the lessons for practice from the theoretical literature, and provide detailed recommendations on what to do. I illustrate the recommendations with three detailed applications.

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    Bibliographic Info

    Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 8049.

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    Length: 67 pages
    Date of creation: Mar 2014
    Date of revision:
    Handle: RePEc:iza:izadps:dp8049

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    Related research

    Keywords: matching methods; propensity score methods; causality; unconfoundedness; potential outcomes; selection on observables;

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    1. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.
    2. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP10/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
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