IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v76y2020i2p664-669.html
   My bibliography  Save this article

Sharp bounds on the relative treatment effect for ordinal outcomes

Author

Listed:
  • Jiannan Lu
  • Yunshu Zhang
  • Peng Ding

Abstract

For ordinal outcomes, the average treatment effect is often ill‐defined and hard to interpret. Echoing Agresti and Kateri, we argue that the relative treatment effect can be a useful measure, especially for ordinal outcomes, which is defined as γ=pr{Yi(1)>Yi(0)}−pr{Yi(1)

Suggested Citation

  • Jiannan Lu & Yunshu Zhang & Peng Ding, 2020. "Sharp bounds on the relative treatment effect for ordinal outcomes," Biometrics, The International Biometric Society, vol. 76(2), pages 664-669, June.
  • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:664-669
    DOI: 10.1111/biom.13148
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13148
    Download Restriction: no

    File URL: https://libkey.io/10.1111/biom.13148?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peng Ding & Tirthankar Dasgupta, 2016. "A Potential Tale of Two-by-Two Tables From Completely Randomized Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 157-168, March.
    2. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    3. Leonardo Grilli & Fabrizia Mealli, 2008. "Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification," Journal of Educational and Behavioral Statistics, , vol. 33(1), pages 111-130, March.
    4. Dungang Liu & Heping Zhang, 2018. "Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 845-854, April.
    5. Alan Agresti & Maria Kateri, 2017. "Ordinal probability effect measures for group comparisons in multinomial cumulative link models," Biometrics, The International Biometric Society, vol. 73(1), pages 214-219, March.
    6. Daniel O. Scharfstein & Charles F. Manski & James C. Anthony, 2004. "On the Construction of Bounds in Prospective Studies with Missing Ordinal Outcomes: Application to the Good Behavior Game Trial," Biometrics, The International Biometric Society, vol. 60(1), pages 154-164, March.
    7. George W. Divine & H. James Norton & Anna E. Barón & Elizabeth Juarez-Colunga, 2018. "The Wilcoxon–Mann–Whitney Procedure Fails as a Test of Medians," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 278-286, July.
    8. Keisuke Hirano & Jack R. Porter, 2012. "Impossibility Results for Nondifferentiable Functionals," Econometrica, Econometric Society, vol. 80(4), pages 1769-1790, July.
    9. Dustin M. Long & Michael G. Hudgens, 2013. "Sharpening Bounds on Principal Effects with Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 812-819, December.
    10. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    11. Jing Cheng, 2009. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome," Biometrics, The International Biometric Society, vol. 65(1), pages 96-103, March.
    12. Joseph P. Romano & Azeem M. Shaikh, 2010. "Inference for the Identified Set in Partially Identified Econometric Models," Econometrica, Econometric Society, vol. 78(1), pages 169-211, 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. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    2. Ho, Kate & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, C.E.P.R. Discussion Papers.
    3. Lukáš Lafférs, 2019. "Identification in Models with Discrete Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 657-696, February.
    4. François Gerard & Miikka Rokkanen & Christoph Rothe, 2020. "Bounds on treatment effects in regression discontinuity designs with a manipulated running variable," Quantitative Economics, Econometric Society, vol. 11(3), pages 839-870, July.
    5. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    6. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, Institute of Labor Economics (IZA).
    7. Gerard, François & Rothe, Christoph & Rokkanen, Miikka, 2016. "Bounds on Treatment Effects in Regression Discontinuity Designs under Manipulation of the Running Variable, with an Application," CEPR Discussion Papers 11668, C.E.P.R. Discussion Papers.
    8. Shanshan Luo & Wei Li & Yangbo He, 2023. "Causal inference with outcomes truncated by death in multiarm studies," Biometrics, The International Biometric Society, vol. 79(1), pages 502-513, March.
    9. Magnac, Thierry, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258, Décembre.
    10. Xintong Wang & Alfonso Flores-Lagunes, 2022. "Conscription and Military Service: Do They Result in Future Violent and Nonviolent Incarcerations and Recidivism?," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1715-1757.
    11. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    12. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    13. Gerard, Francois & Rokkanen, Miikka & Rothe, Christoph, 2015. "Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable," IZA Discussion Papers 9604, Institute of Labor Economics (IZA).
    14. Lu, Jiannan, 2018. "On the partial identification of a new causal measure for ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 1-7.
    15. Kaido, Hiroaki, 2017. "Asymptotically Efficient Estimation Of Weighted Average Derivatives With An Interval Censored Variable," Econometric Theory, Cambridge University Press, vol. 33(5), pages 1218-1241, October.
    16. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Testing Many Moment Inequalities," CeMMAP working papers 65/13, Institute for Fiscal Studies.
    17. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    18. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    19. Andrew Chesher & Adam Rosen, 2015. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers 63/15, Institute for Fiscal Studies.
    20. Donald S. Poskitt & Xueyan Zhao, 2023. "Bootstrap Hausdorff Confidence Regions for Average Treatment Effect Identified Sets," Monash Econometrics and Business Statistics Working Papers 9/23, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    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:bla:biomet:v:76:y:2020:i:2:p:664-669. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.