IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i1p96-103.html

Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome

Author

Listed:
  • Jing Cheng

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:1:p:96-103
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01020.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. András Vargha & Harold D. Delaney, 1998. "The Kruskal-Wallis Test and Stochastic Homogeneity," Journal of Educational and Behavioral Statistics, , vol. 23(2), pages 170-192, June.
    2. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    3. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    4. Jing Cheng & Dylan S. Small, 2006. "Bounds on causal effects in three‐arm trials with non‐compliance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 815-836, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stuart G. Baker, 2011. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach," Biometrics, The International Biometric Society, vol. 67(1), pages 319-323, March.
    2. Lu, Jiannan & Ding, Peng & Dasgupta, Tirthankar, 2015. "Construction of alternative hypotheses for randomization tests with ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 348-355.
    3. 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.
    4. G. Baker Stuart & S. Lindeman Karen, 2013. "Revisiting a Discrepant Result: A Propensity Score Analysis, the Paired Availability Design for Historical Controls, and a Meta-Analysis of Randomized Trials," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 51-82, June.
    5. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy," Papers 2005.09130, arXiv.org, revised Jun 2020.
    6. 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.
    7. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1615-1635, October.
    8. Ailei Ding & Hanmei Sun, 2025. "Bounds on Causal Effects Based on Expectations in Ordered-Outcome Models," Mathematics, MDPI, vol. 13(19), pages 1-15, September.
    9. 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.
    10. Jing Cheng, 2011. "The authors replied as follows:," Biometrics, The International Biometric Society, vol. 67(1), pages 323-325, March.
    11. 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.

    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. 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.
    2. Joshua D. Angrist & Sarah R. Cohodes & Susan M. Dynarski & Parag A. Pathak & Christopher R. Walters, 2016. "Stand and Deliver: Effects of Boston's Charter High Schools on College Preparation, Entry, and Choice," Journal of Labor Economics, University of Chicago Press, vol. 34(2), pages 275-318.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP57/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    5. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    6. Lars Kirkebøen & Edwin Leuven & Magne Mogstad, 2014. "Field of Study, Earnings, and Self-Selection," NBER Working Papers 20816, National Bureau of Economic Research, Inc.
    7. Byeong Yeob Choi, 2024. "Instrumental variable estimation of weighted local average treatment effects," Statistical Papers, Springer, vol. 65(2), pages 737-770, April.
    8. 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.
    9. Humlum, Anders & Munch, Jakob R. & Rasmussen, Mette, 2023. "What Works for the Unemployed? Evidence from Quasi-Random Caseworker Assignments," IZA Discussion Papers 16033, IZA Network @ LISER.
    10. Michael R.M. Abrigo & Timothy J. Halliday & Teresa Molina, 2022. "Expanding health insurance for the elderly of the Philippines," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 500-520, April.
    11. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, IZA Network @ LISER.
    12. Zhang, Hongliang, 2016. "Identification of treatment effects under imperfect matching with an application to Chinese elite schools," Journal of Public Economics, Elsevier, vol. 142(C), pages 56-82.
    13. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    14. Bitler, Marianne P. & Gelbach, Jonah B. & Hoynes, Hilary W., 2008. "Distributional impacts of the Self-Sufficiency Project," Journal of Public Economics, Elsevier, vol. 92(3-4), pages 748-765, April.
    15. Hui Nie & Jing Cheng & Dylan S. Small, 2011. "Inference for the Effect of Treatment on Survival Probability in Randomized Trials with Noncompliance and Administrative Censoring," Biometrics, The International Biometric Society, vol. 67(4), pages 1397-1405, December.
    16. Esfandiar Maasoumi & Daniel L. Millimet & Dipanwita Sarkar, 2009. "Who Benefits from Marriage?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 1-33, February.
    17. Sönke Hendrik Matthewes & Guglielmo Ventura, 2022. "On Track to Success? Returns to vocational education against different alternatives," CVER Research Papers 038, Centre for Vocational Education Research.
    18. Cañón Salazar Carlos Iván, 2016. "Distributional Policy Effects with Many Treatment Outcomes," Working Papers 2016-01, Banco de México.
    19. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    20. Abhijit V. Banerjee & Esther Duflo, 2009. "The Experimental Approach to Development Economics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 151-178, May.

    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:65:y:2009:i:1:p:96-103. 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.