Skewness-Robust Causal Discovery in Location-Scale Noise Models
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Abstract (arXiv)
To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model. Crucially, we need to be able to distinguish cause X from effect Y in bivariate models, that is, distinguish the two graphs X → Y and Y → X. Location-scale noise models (LSNMs), in which the effect Y is modeled based on the cause X as Y = f(X) + g(X)N, form a flexible class of models that is general and identifiable in most cases. Estimating these models for arbitrary noise terms N, however, is challenging. Therefore, practical estimators are typically restricted to symmetric distributions, such as the normal distribution. As we showcase in this paper, when N is a skewed random variable, which is likely in real-world domains, the reliability of these approaches decreases. To approach this limitation, we propose SkewD, a likelihood-based algorithm for bivariate causal discovery under LSNMs with skewed noise distributions. SkewD extends the usual normal-distribution framework to the skew-normal setting, enabling reliable inference under symmetric and skewed noise. For parameter estimation, we employ a combination of a heuristic search and an expectation conditional maximization algorithm. We evaluate SkewD on novel synthetically generated datasets with skewed noise as well as established benchmark datasets. Throughout our experiments, SkewD exhibits a strong performance and, in comparison to prior work, remains robust under high skewness.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 71 references that are in the map. First posted on arXiv 18 Nov 2025.
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22 of 71 references.
References · 22
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2018A Tutorial on Bayesian Optimizationcited by 744
- 2016The CMA Evolution Strategy: A Tutorialcited by 377
- 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
- 2019Gradient-Based Neural DAG Learningcited by 227
- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
- 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
- 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
- 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
- 2020Causal Autoregressive Flowscited by 99
- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
- 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
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- 2017Telling Cause from Effect using MDL-based Local and Global Regressioncited by 21
- 2023Bivariate Causal Discovery using Bayesian Model Selectioncited by 12
- 2024cmaes: A Simple yet Practical Python Library for CMA-EScited by 12
- 2022On the pitfalls of Gaussian likelihood scoring for causal discoverycited by 7
- 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testingcited by 7
- 2023Heteroscedastic Causal Structure Learningcited by 7
- 2024A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discoverycited by 7
- 2023Identifiability of causal graphs under nonadditive conditionally parametric causal modelscited by 6
- 2023Robust Estimation of Causal Heteroscedastic Noise Modelscited by 6
- 2023Order-based Structure Learning with Normalizing Flowscited by 5
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