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Heterogeneous Treatment Effects : Instrumental Variables Without Monotonicity?

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  • Klein, T.J.

    (Tilburg University, School of Economics and Management)

Abstract

Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a local average treatment effect parameter using instrumental variables. More recently, Heckman and Vytlacil (1999) suggested the estimation of a variety of treatment effect parameters using a local version of their approach. We investigate the sensitivity of the respective estimates to random departures from monotonicity. Approximations to the respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study.
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Suggested Citation

  • Klein, T.J., 2008. "Heterogeneous Treatment Effects : Instrumental Variables Without Monotonicity?," Other publications TiSEM e015611a-6b97-4cd5-8dc8-7, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:e015611a-6b97-4cd5-8dc8-7818c6c5e39e
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    as
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    Cited by:

    1. Chaisemartin, Clément de, 2014. "Tolerating defiance? Local average treatment effects without monotonicity," CAGE Online Working Paper Series 197, Competitive Advantage in the Global Economy (CAGE).
    2. Tobias Klein, 2013. "College education and wages in the U.K.: estimating conditional average structural functions in nonadditive models with binary endogenous variables," Empirical Economics, Springer, vol. 44(1), pages 135-161, February.
    3. Ben Edwards & Mario Fiorini & Katrien Stevens & Matthew Taylor, 2013. "Is Monotonicity in an IV and RD Design Testable? No, But You Can Still Check on it," Working Paper Series 7, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    5. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    6. Zhang, Zhijian & Wang, Xueyuan, 2022. "Birthplace diversity and private giving: Evidence from China," China Economic Review, Elsevier, vol. 74(C).
    7. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    8. 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).
    9. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
    10. Ivan Zilic, 2018. "Effect of forced displacement on health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 889-906, June.
    11. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    12. Gautier, Eric & Hoderlein, Stefan, 2011. "A triangular treatment effect model with random coefficients in the selection equation," TSE Working Papers 15-598, Toulouse School of Economics (TSE), revised 25 Aug 2015.
    13. 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.
    14. Will Dobbie & Jae Song, 2015. "Debt Relief and Debtor Outcomes: Measuring the Effects of Consumer Bankruptcy Protection," American Economic Review, American Economic Association, vol. 105(3), pages 1272-1311, March.
    15. Clément de Chaisemartin, 2017. "Tolerating defiance? Local average treatment effects without monotonicity," Quantitative Economics, Econometric Society, vol. 8(2), pages 367-396, July.
    16. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2012. "Late Again with Defiers," PSE Working Papers halshs-00699646, HAL.
    17. Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    18. repec:jku:cdlwps:2015_08 is not listed on IDEAS
    19. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.
    20. Bernal Lobato, N., 2014. "Essays in applied microeconomics," Other publications TiSEM 9b638b3d-2f83-452a-b2c8-c, Tilburg University, School of Economics and Management.
    21. Lechner, Michael, 2013. "Treatment effects and panel data," Economics Working Paper Series 1314, University of St. Gallen, School of Economics and Political Science.
    22. Bernal, Noelia & Carpio, Miguel A. & Klein, Tobias J., 2017. "The effects of access to health insurance: Evidence from a regression discontinuity design in Peru," Journal of Public Economics, Elsevier, vol. 154(C), pages 122-136.
    23. Fiorini, Mario & Katrien Stevens, 2014. "Assessing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2014-13, University of Sydney, School of Economics.
    24. Toru Kitagawa & Martin Nybom & Jan Stuhler, 2018. "Measurement error and rank correlations," CeMMAP working papers CWP28/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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