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Testing for nonparametric identification of causal effects in the presence of a quasi-instrument

Author

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  • de Luna, Xavier

    (Umeå School of Business and Economics, Umeå University)

  • Johansson, Per

    (IFAU - Institute for Evaluation of Labour Market and Education Policy)

Abstract

The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption (exogeneity of the treatment). In this paper, we define variables which we call quasi-instruments because they allow us to test for the unconfoundedness assumption although they do not necessarily yield nonparametric identification of the average causal effect. A quasi-instrument is defined as an instrument except for that its relation to the treatment is allowed to be confounded by unobservables, thereby resulting in a wider range of potential applications. We propose a test for the unconfoundedness assumption based on a quasi-instrument and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

Suggested Citation

  • de Luna, Xavier & Johansson, Per, 2012. "Testing for nonparametric identification of causal effects in the presence of a quasi-instrument," Working Paper Series 2012:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2012_014
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    References listed on IDEAS

    as
    1. Fredriksson, Peter & Johansson, Per, 2008. "Dynamic Treatment Assignment," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 435-445.
    2. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
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    7. de Luna, Xavier & Johansson, Per & Sjöstedt-de Luna, Sara, 2010. "Bootstrap inference for K-nearest neighbour matching estimators," Working Paper Series 2010:13, IFAU - Institute for Evaluation of Labour Market and Education Policy.
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    9. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 395-415, July.
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    11. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
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    Cited by:

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    2. Gerry H. Makepeace & Michael J. Peel, 2013. "Combining information from Heckman and matching estimators: testing and controlling for hidden bias," Economics Bulletin, AccessEcon, vol. 33(3), pages 2422-2436.

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    More about this item

    Keywords

    Quasi-instrument; causal effects;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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