Causal Order Discovery based on Monotonic SCMs Article Swipe
Ali Izadi
,
Martin Ester
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19870
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19870
In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observational data. While existing approaches either assume prior knowledge about the causal order or use complex optimization techniques to impose sparsity in the Jacobian of Triangular Monotonic Increasing maps, our work introduces a novel sequential procedure that directly identifies the causal order by iteratively detecting the root variable. This method eliminates the need for sparsity assumptions and the associated optimization challenges, enabling the identification of a unique SCM without the need for multiple independence tests to break the Markov equivalence class. We demonstrate the effectiveness of our approach in sequentially finding the root variable, comparing it to methods that maximize Jacobian sparsity.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.19870
- https://arxiv.org/pdf/2410.19870
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- DOI
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https://doi.org/10.48550/arxiv.2410.19870Digital Object Identifier
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Causal Order Discovery based on Monotonic SCMsWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-24Full publication date if available
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Ali Izadi, Martin EsterList of authors in order
- Landing page
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https://arxiv.org/abs/2410.19870Publisher landing page
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https://arxiv.org/pdf/2410.19870Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.19870Direct OA link when available
- Concepts
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Monotonic function, Order (exchange), Computer science, Mathematics, Econometrics, Business, Mathematical analysis, FinanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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