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

Author

Listed:
  • Strobl Eric V.
  • Visweswaran Shyam

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

  • Zhang Kun

    (6612Carnegie Mellon University, Department of Philosophy, 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. & Visweswaran Shyam & Zhang Kun, 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Christoph Breunig & Patrick Burauel, 2021. "Testability of Reverse Causality without Exogeneous Variation," Papers 2107.05936, arXiv.org.

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