IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v119y2024i547p2178-2189.html
   My bibliography  Save this article

Unveiling the Unobservable: Causal Inference on Multiple Derived Outcomes

Author

Listed:
  • Yumou Qiu
  • Jiarui Sun
  • Xiao-Hua Zhou

Abstract

In many applications, the interest is in treatment effects on random quantities of subjects, where those random quantities are not directly observable but can be estimated based on data from each subject. In this article, we propose a general framework for conducting causal inference in a hierarchical data generation setting. The identifiability of causal parameters of interest is shown under a condition on the biasedness of subject level estimates and an ignorability condition on the treatment assignment. Estimation of the treatment effects is constructed by inverse propensity score weighting on the estimated subject level parameters. A multiple testing procedure able to control the false discovery proportion is proposed to identify the nonzero treatment effects. Theoretical results are developed to investigate the proposed procedure, and numerical simulations are carried out to evaluate its empirical performance. A case study of medication effects on brain functional connectivity of patients with Autism spectrum disorder (ASD) using fMRI data is conducted to demonstrate the utility of the proposed method. Supplementary materials for this article are available online.

Suggested Citation

  • Yumou Qiu & Jiarui Sun & Xiao-Hua Zhou, 2024. "Unveiling the Unobservable: Causal Inference on Multiple Derived Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(547), pages 2178-2189, July.
  • Handle: RePEc:taf:jnlasa:v:119:y:2024:i:547:p:2178-2189
    DOI: 10.1080/01621459.2023.2252135
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2023.2252135
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2023.2252135?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:jnlasa:v:119:y:2024:i:547:p:2178-2189. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.