On the pitfalls of Gaussian likelihood scoring for causal discovery

Schultheiss Christoph () and Bühlmann Peter ()
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Schultheiss Christoph: Seminar for Statistics, Department of Mathematics, ETH Zürich, Switzerland
Bühlmann Peter: Seminar for Statistics, Department of Mathematics, ETH Zürich, Switzerland

Journal of Causal Inference, 2023, vol. 11, issue 1, 11

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.

Keywords: graphical models; model misspecification; non-parametric regression; structural causal models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:11:y:2023:i:1:p:11:n:1

DOI: 10.1515/jci-2022-0068

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