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On causal discovery with an equal-variance assumption

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  • Wenyu Chen
  • Mathias Drton
  • Y Samuel Wang

Abstract

SummaryPrior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.

Suggested Citation

  • Wenyu Chen & Mathias Drton & Y Samuel Wang, 2019. "On causal discovery with an equal-variance assumption," Biometrika, Biometrika Trust, vol. 106(4), pages 973-980.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:4:p:973-980.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz049
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    Cited by:

    1. Park, Gunwoong & Kim, Yesool, 2021. "Learning high-dimensional Gaussian linear structural equation models with heterogeneous error variances," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    2. Choi, Semin & Kim, Yesool & Park, Gunwoong, 2023. "Densely connected sub-Gaussian linear structural equation model learning via ℓ1- and ℓ2-regularized regressions," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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