IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v8y2019i1p21n1.html
   My bibliography  Save this article

Unifying Gaussian LWF and AMP Chain Graphs to Model Interference

Author

Listed:
  • Peña Jose M.

    (IDA, Linköping University, Linköping, Sweden)

Abstract

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.

Suggested Citation

  • Peña Jose M., 2019. "Unifying Gaussian LWF and AMP Chain Graphs to Model Interference," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 1-21, January.
    • Handle: RePEc:bpj:causin:v:8:y:2019:i:1:p:21:n:1
    DOI: 10.1515/jci-2018-0034
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2018-0034
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2018-0034?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. Mathias Drton & Michael Eichler, 2006. "Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 247-257, June.
    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. Peña Jose M., 2020. "Unifying Gaussian LWF and AMP Chain Graphs to Model Interference," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 1-21, January.
    2. Søren Højsgaard & Steffen L. Lauritzen, 2008. "Graphical Gaussian models with edge and vertex symmetries," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1005-1027, November.
    3. Fitch, A. Marie & Jones, Beatrix, 2012. "The cost of using decomposable Gaussian graphical models for computational convenience," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2430-2441.

    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:bpj:causin:v:8:y:2019:i:1:p:21:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.