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Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

Author

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  • de Luna Xavier

    (Department of Statistics, Umeå School of Business and Economics, Umeå University, SE-90187 Umeå, Sweden)

  • Johansson Per

    (Department of Economics, Uppsala University, Uppsala, Sweden Institute for Evaluation of Labour Market and Education Policy, Uppsala, Sweden)

Abstract

The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced 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, 2014. "Testing for the Unconfoundedness Assumption Using an Instrumental Assumption," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 187-199, September.
    • Handle: RePEc:bpj:causin:v:2:y:2014:i:2:p:13:n:2
    DOI: 10.1515/jci-2013-0011
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    2. Pathric Hägglund & Per Johansson & Lisa Laun, 2020. "The Impact of CBT on Sick Leave and Health," Evaluation Review, , vol. 44(2-3), pages 185-217, April.
    3. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    4. Khalil, Umair & Yıldız, Neşe, 2022. "A test of the selection on observables assumption using a discontinuously distributed covariate," Journal of Econometrics, Elsevier, vol. 226(2), pages 423-450.
    5. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. Eva Deuchert & Martin Huber, 2017. "A Cautionary Tale About Control Variables in IV Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(3), pages 411-425, June.
    7. Al-Shaer, Habiba & Uyar, Ali & Kuzey, Cemil & Karaman, Abdullah S., 2023. "Do shareholders punish or reward excessive CSR engagement? Moderating effect of cash flow and firm growth," International Review of Financial Analysis, Elsevier, vol. 88(C).
    8. Nicolas Apfel & Julia Hatamyar & Martin Huber & Jannis Kueck, 2024. "Learning control variables and instruments for causal analysis in observational data," Papers 2407.04448, arXiv.org.
    9. Harsh Parikh & Marco Morucci & Vittorio Orlandi & Sudeepa Roy & Cynthia Rudin & Alexander Volfovsky, 2023. "A Double Machine Learning Approach to Combining Experimental and Observational Data," Papers 2307.01449, arXiv.org, revised Apr 2024.
    10. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.
    11. Hägglund, Pathric & Johansson, Per & Laun, Lisa, 2015. "Rehabilitation of mental illness and chronic pain – the impact on sick leave and health," Working Paper Series 2015:22, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    12. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.

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