Learning Structural Causal Models from Ordering: Identifiable Flow Models Article Swipe
In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization steps. We propose design improvements that enable simultaneous learning of all causal mechanisms and reduce abduction and prediction complexity to linear O(n) relative to the number of layers, independent of the number of causal variables. Empirically, we demonstrate that our method outperforms previous state-of-the-art approaches and delivers consistent performance across a wide range of structural causal models in answering observational, interventional, and counterfactual questions. Additionally, our method achieves a significant reduction in computational time compared to existing diffusion-based techniques, making it practical for large structural causal models.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.09843
- https://arxiv.org/pdf/2412.09843
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405433011
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405433011Canonical identifier for this work in OpenAlex
- Title
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Learning Structural Causal Models from Ordering: Identifiable Flow ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-13Full publication date if available
- Authors
-
Minh Le, Kien Do, Truyen TranList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.09843Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.09843Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.09843Direct OA link when available
- Concepts
-
Causal model, Flow (mathematics), Econometrics, Psychology, Computer science, Economics, Mathematics, Mechanics, Physics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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