IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v076i12.html
   My bibliography  Save this article

Identifying Causal Effects with the R Package causaleffect

Author

Listed:
  • Tikka, Santtu
  • Karvanen, Juha

Abstract

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl (2006b) constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples.

Suggested Citation

  • Tikka, Santtu & Karvanen, Juha, 2017. "Identifying Causal Effects with the R Package causaleffect," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i12).
    • Handle: RePEc:jss:jstsof:v:076:i12
    DOI: http://hdl.handle.net/10.18637/jss.v076.i12
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v076i12/v76i12.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v076i12/causaleffect_1.3.3.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v076i12/g1.graphml
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v076i12/v76i12.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v076.i12?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
    ---><---

    Citations

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


    Cited by:

    1. Raputsoane, Leroi, 2018. "Leader followership in monetary policy coordination," MPRA Paper 121903, University Library of Munich, Germany.
    2. Aurora C. Schmidt & Christopher J. Cameron & Corey Lowman & Joshua Brulé & Amruta J. Deshpande & Seyyed A. Fatemi & Vladimir Barash & Ariel M. Greenberg & Cash J. Costello & Eli S. Sherman & Rohit Bha, 2023. "Searching for explanations: testing social scientific methods in synthetic ground-truthed worlds," Computational and Mathematical Organization Theory, Springer, vol. 29(1), pages 156-187, March.
    3. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    4. Raputsoane, Leroi, 2018. "Monetary policy coordination leader followership," MPRA Paper 85684, University Library of Munich, Germany.
    5. Brathwaite, Timothy & Walker, Joan L., 2018. "Causal inference in travel demand modeling (and the lack thereof)," Journal of choice modelling, Elsevier, vol. 26(C), pages 1-18.
    6. Lauri Valkonen & Jouni Helske & Juha Karvanen, 2023. "Estimating the causal effect of timing on the reach of social media posts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 493-507, June.
    7. Jouni Helske & Santtu Tikka & Juha Karvanen, 2021. "Estimation of causal effects with small data in the presence of trapdoor variables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1030-1051, July.

    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:jss:jstsof:v:076:i12. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.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.