IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0252.html

Local Instrumental Variables

Author

Listed:
  • James J. Heckman
  • Edward J. Vytlacil

Abstract

This paper unites the treatment effect literature and the latent variable literature. The economic questions answered by the commonly used treatment effect parameters are considered. We demonstrate how the marginal treatment effect parameter can be used in a latent variable framework to generate the average treatment effect, the effect of treatment on the treated and the local average treatment effect, thereby establishing a new relationship among these parameters. The method of local instrumental variables directly estimates the marginal treatment effect parameters, and thus can be used to estimate all of the conventional treatment effect parameters when the index condition holds and the parameters are identified. When they are not, the method of local instrumental variables can be used to produce bounds on the parameters with the width of the bounds depending on the width of the support for the index generating the choice of the observed potential outcome.

Suggested Citation

  • James J. Heckman & Edward J. Vytlacil, 2000. "Local Instrumental Variables," NBER Technical Working Papers 0252, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0252
    Note: TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0252.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    2. Bodory, Hugo & Huber, Martin, 2018. "The causalweight package for causal inference in R," FSES Working Papers 493, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Michal Kolesár, 2013. "Estimation in an Instrumental Variables Model With Treatment Effect Heterogeneity," Working Papers 2013-2, Princeton University. Economics Department..
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP77/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. David Card & David S. Lee & Zhuan Pei & Andrea Weber, 2015. "Inference on Causal Effects in a Generalized Regression Kink Design," Econometrica, Econometric Society, vol. 83, pages 2453-2483, November.
    6. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2003. "Measuring Heterogeneity in the Returns to Education in Norway Using Educational Reforms," IZA Discussion Papers 815, Institute of Labor Economics (IZA).
    7. Woutersen, Tiemen & Hausman, Jerry A., 2019. "Increasing the power of specification tests," Journal of Econometrics, Elsevier, vol. 211(1), pages 166-175.
    8. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    9. Pilar Garcia‐Gomez & Pierre Koning & Owen O'Donnell & Carlos Riumalló‐Herl, 2025. "Selective exercise of discretion in disability insurance awards," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 44(3), pages 816-835, June.
    10. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
    11. Elisa Gerten & Michael Beckmann & Elisa Gerten & Matthias Kräkel, 2022. "Information and Communication Technology, Hierarchy, and Job Design," ECONtribute Discussion Papers Series 189, University of Bonn and University of Cologne, Germany.
    12. Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum & Winnie van Dijk, 2024. "Eviction and Poverty in American Cities," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 57-120.
    13. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    14. Moffitt, Robert A. & Zahn, Matthew V., 2022. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," Economics Working Paper Archive 66674, The Johns Hopkins University,Department of Economics.
    15. Battistin, Erich & De Nadai, Michele & Vuri, Daniela, 2017. "Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools," Journal of Econometrics, Elsevier, vol. 200(2), pages 344-362.
    16. Yu‐Chang Chen & Haitian Xie, 2022. "Global Representation of the Conditional LATE Model: A Separability Result," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 789-798, August.
    17. Marc HENRY & Ismael MOURIFIÉ, 2013. "Nonparametric Sharp Bounds For Payoffs In 2 × 2 Games," Working Papers tecipa-500, University of Toronto, Department of Economics.
    18. Salanié, Bernard & Lee, Sokbae, 2020. "Filtered and Unfiltered Treatment Effects with Targeting Instruments," CEPR Discussion Papers 15092, C.E.P.R. Discussion Papers.
    19. Pedro Carneiro & Michael Lokshin & Nithin Umapathi, 2017. "Average and Marginal Returns to Upper Secondary Schooling in Indonesia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 16-36, January.
    20. Christophe Gaillac & Eric Gautier, 2021. "Nonparametric classes for identification in random coefficients models when regressors have limited variation," Working Papers hal-03231392, HAL.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0252. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.