On the pitfalls of Gaussian likelihood scoring for causal discovery
Abstract: We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising negative result for Gaussian likelihood scoring in combination with nonparametric regression methods.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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On the pitfalls of Gaussian likelihood scoring for causal discovery ; volume:11 ; number:1 ; year:2023 ; extent:11
Journal of causal inference ; 11, Heft 1 (2023) (gesamt 11)
- Urheber
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Schultheiss, Christoph
Bühlmann, Peter
- DOI
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10.1515/jci-2022-0068
- URN
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urn:nbn:de:101:1-2023051114300067671801
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 11:01 MESZ
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Beteiligte
- Schultheiss, Christoph
- Bühlmann, Peter
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