IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v7y2019i1p24n7.html
   My bibliography  Save this article

Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

Author

Listed:
  • Strobl Eric V.

    (6614University of Pittsburgh, Department of Biomedical Informatics, Pittsburgh, United States)

  • Zhang Kun

    (6612Carnegie Mellon University, Department of Philosophy, Pittsburgh, United States)

  • Visweswaran Shyam

    (6614University of Pittsburgh, Department of Biomedical Informatics, Pittsburgh, United States)

Abstract

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test scales at least quadratically with sample size. We therefore devise two relaxations called the Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT) which both approximate KCIT by utilizing random Fourier features. In practice, both of the proposed tests scale linearly with sample size and return accurate p-values much faster than KCIT in the large sample size context. CCD algorithms run with RCIT or RCoT also return graphs at least as accurate as the same algorithms run with KCIT but with large reductions in run time.

Suggested Citation

  • Strobl Eric V. & Zhang Kun & Visweswaran Shyam, 2019. "Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery," Journal of Causal Inference, De Gruyter, vol. 7(1), pages 1-24, March.
    • Handle: RePEc:bpj:causin:v:7:y:2019:i:1:p:24:n:7
    DOI: 10.1515/jci-2018-0017
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2018-0017
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2018-0017?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. Bruce Lindsay & Ramani Pilla & Prasanta Basak, 2000. "Moment-Based Approximations of Distributions Using Mixtures: Theory and Applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 215-230, June.
    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. Christoph Breunig & Patrick Burauel, 2021. "Testability of Reverse Causality Without Exogenous Variation," Papers 2107.05936, arXiv.org, revised Apr 2024.

    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. Mnatsakanov, Robert M., 2008. "Hausdorff moment problem: Reconstruction of distributions," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1612-1618, September.
    2. A. Philip Dawid & Monica Musio & Laura Ventura, 2016. "Minimum Scoring Rule Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 123-138, March.
    3. Stephen Portnoy, 2015. "Maximizing Probability Bounds Under Moment-Matching Restrictions," The American Statistician, Taylor & Francis Journals, vol. 69(1), pages 41-44, February.
    4. Marco Marozzi, 2014. "The multisample Cucconi test," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 209-227, June.
    5. Jae-Kyung Woo & Haibo Liu, 2018. "Discounted Aggregate Claim Costs Until Ruin in the Discrete-Time Renewal Risk Model," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1285-1318, December.
    6. Farhood Rismanchian & Young Hoon Lee, 2018. "Moment-based approximations for first- and second-order transient performance measures of an unreliable workstation," Operational Research, Springer, vol. 18(1), pages 75-95, April.
    7. Seri, Raffaello, 2022. "Computing the asymptotic distribution of second-order U- and V-statistics," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    8. Emanuele Taufer & Sudip Bose & Aldo Tagliani, 2009. "Optimal predictive densities and fractional moments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(1), pages 57-71, January.

    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:7:y:2019:i:1:p:24:n:7. 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.