IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2403.18503.html
   My bibliography  Save this paper

Distributional Treatment Effect with Latent Rank Invariance

Author

Listed:
  • Myungkou Shin

Abstract

Treatment effect heterogeneity is of a great concern when evaluating the treatment. However, even with a simple case of a binary random treatment, the distribution of treatment effect is difficult to identify due to the fundamental limitation that we cannot observe both treated potential outcome and untreated potential outcome for a given individual. This paper assumes a conditional independence assumption that the two potential outcomes are independent of each other given a scalar latent variable. Using two proxy variables, we identify conditional distribution of the potential outcomes given the latent variable. To pin down the location of the latent variable, we assume strict monotonicty on some functional of the conditional distribution; with specific example of strictly increasing conditional expectation, we label the latent variable as 'latent rank' and motivate the identifying assumption as 'latent rank invariance.'

Suggested Citation

  • Myungkou Shin, 2024. "Distributional Treatment Effect with Latent Rank Invariance," Papers 2403.18503, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2403.18503
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2403.18503
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tetsuya Kaji & Jianfei Cao, 2023. "Assessing Heterogeneity of Treatment Effects," Papers 2306.15048, arXiv.org.
    2. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    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. Cécile Couharde & Rémi Generoso, 2024. "Assessing the Impact of National Air Quality Standards on Agricultural Land Values: Insights from Corn and Soybean Regions," Working Papers hal-04503777, HAL.
    2. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    3. Frimmel, Wolfgang & Schmidpeter, Bernhard & Wiesinger, Rene & Winter-Ebmer, Rudolf, 2023. "External Pay Transparency and the Gender Wage Gap," IZA Discussion Papers 16233, Institute of Labor Economics (IZA).
    4. Marianna Schaubert, 2023. "Do Alimony Regulations Matter Inside Marriage? Evidence from the 2008 Reform of the German Maintenance Law," Journal of Labor Research, Springer, vol. 44(1), pages 145-178, June.
    5. Brantly Callaway & Tong Li & Joel Rodrigue & Yuya Sasaki & Yong Tan, 2024. "Regulation, Emissions and Productivity: Evidence from China’s Eleventh Five-Year Plan," Staff Working Papers 24-7, Bank of Canada.
    6. Jessica Ya Sun, 2020. "Welfare consequences of access to health insurance for rural households: Evidence from the New Cooperative Medical Scheme in China," Health Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 337-352, March.
    7. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    8. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org.
    9. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    10. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    11. Oliver Cassagneau-Francis & Robert Gary-Bobo & Julie Pernaudet & Jean-Marc Robin, 2022. "A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages," Working Papers hal-03869547, HAL.
    12. Afrouz Azadikhah Jahromi & Brantly Callaway, 2022. "Heterogeneous Effects of Job Displacement on Earnings," Empirical Economics, Springer, vol. 62(1), pages 213-245, January.
    13. Callaway, Brantly, 2021. "Bounds on distributional treatment effect parameters using panel data with an application on job displacement," Journal of Econometrics, Elsevier, vol. 222(2), pages 861-881.
    14. Jeffrey Penney & Steven Lehrer & Emilia Galan, 2024. "Mandatory minimum sentencing and its effect on sentencing distributions: Evidence from Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(1), pages 55-77, February.
    15. Daniel Kaliski, 2023. "Identifying the impact of health insurance on subgroups with changing rates of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2098-2112, September.
    16. Jonathan Roth & Pedro H. C. Sant'Anna, 2023. "When Is Parallel Trends Sensitive to Functional Form?," Econometrica, Econometric Society, vol. 91(2), pages 737-747, March.
    17. Holm, Mathilde Lund & Fallesen, Peter & Heinesen, Eskil, 2023. "The effects of parental union dissolution on children’s test scores," SocArXiv p2qgk, Center for Open Science.
    18. Nicholas Gunby & Tom Coupé, 2023. "Weather-Related Home Damage and Subjective Well-Being," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(2), pages 409-438, February.
    19. Xin Liu, 2024. "A quantile-based nonadditive fixed effects model," Papers 2405.03826, arXiv.org.
    20. Brantly Callaway, 2017. "Job Displacement during the Great Recession: Tight Bounds on Distributional Treatment Effect Parameters using Panel Data," DETU Working Papers 1703, Department of Economics, Temple University.

    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:arx:papers:2403.18503. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.