LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery
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Abstract (arXiv)
Inferring causal relationships from observational data is crucial when experiments are costly or infeasible. Additive noise models (ANMs) enable unique directed acyclic graph (DAG) identification, but existing sample-efficient ANM methods often rely on restrictive assumptions on the data generating process, limiting their applicability to real-world settings. We propose local search in additive noise models, LoSAM, a topological ordering method for learning a unique DAG in ANMs with mixed causal mechanisms and general noise distributions. We introduce new causal substructures and criteria for identifying roots and leaves, enabling efficient top-down learning. We prove asymptotic consistency and polynomial runtime, ensuring scalability and sample efficiency. We test LoSAM on synthetic and real-world data, demonstrating state-of-the-art performance across all mixed mechanism settings.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 21 of its 60 references that are in the map. First posted on arXiv 15 Oct 2024.
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21 of 60 references.
References · 21
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- 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
- 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 137
- 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 110
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- 2013Learning Sparse Causal Models is not NP-hardcited by 49
9 more
- 2023Scalable Causal Discovery with Score Matchingcited by 44
- 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 43
- 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
- 2023Assumption violations in causal discovery and the robustness of score matchingcited by 28
- 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
- 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relationscited by 13
- 2023Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairscited by 9
- 2024Score matching through the roof: linear, nonlinear, and latent variables causal discoverycited by 9
- 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Modelscited by 5
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