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Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT


Author Info

  • Stephen G. Donald

    (Department of Economics, University of Texas at Austin)

  • Yu-Chin Hsu

    (Institute of Economics, Academia Sinica, Taipei, Taiwan)

  • Robert P. Lieli

    (Department of Economics, Central European University, Budapest and the National Bank of Hungary)

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    We propose inverse probability weighted estimators for the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions with covariates. We show that these estimators are asymptotically normal and effcient. When the (binary) instrument satisfies one-sided non-compliance, we propose a Durbin- Wu-Hausman-type test of whether treatment assignment is unconfounded conditional on some observables. The test is based on the fact that under one-sided non-compliance LATT coincides with the average treatment effect for the treated (ATT). We conduct Monte Carlo simulations to demonstrate, among other things, that part of the theoretical effciency gain afforded by unconfoundedness in estimating ATT survives pre-testing. We illustrate the implementation of the test on data from training programs administered under the Job Training Partnership Act in the U.S.

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    Bibliographic Info

    Paper provided by Institute of Economics, Academia Sinica, Taipei, Taiwan in its series IEAS Working Paper : academic research with number 12-A017.

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    Length: 39 pages
    Date of creation: Dec 2012
    Date of revision: Jan 2014
    Handle: RePEc:sin:wpaper:12-a017

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    Keywords: local average treatment effect; instrumental variables; unconfoundedness; inverse probability weighted estimation; nonparametric estimation;

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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. Huber, Martin, 2013. "A simple test for the ignorability of non-compliance in experiments," Economics Working Paper Series 1312, University of St. Gallen, School of Economics and Political Science.
    3. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers, Department of Economics, Central European University 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.


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