Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows Article Swipe
Nu Hoang
,
Bao Duong
,
Thin Nguyen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.04980
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.04980
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility constraint required in PNL models remains challenging in existing studies. To address this problem, we introduce CAF-PoNo (Causal discovery via Normalizing Flows for Post-Nonlinear models), harnessing the power of the normalizing flows architecture to enforce the crucial invertibility constraint in PNL models. Through normalizing flows, our method precisely reconstructs the hidden noise, which plays a vital role in cause-effect identification through statistical independence testing. Furthermore, the proposed approach exhibits remarkable extensibility, as it can be seamlessly expanded to facilitate multivariate causal discovery via causal order identification, empowering us to efficiently unravel complex causal relationships. Extensive experimental evaluations on both simulated and real datasets consistently demonstrate that the proposed method outperforms several state-of-the-art approaches in both bivariate and multivariate causal discovery tasks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.04980
- https://arxiv.org/pdf/2407.04980
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400479885
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400479885Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.04980Digital Object Identifier
- Title
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Enabling Causal Discovery in Post-Nonlinear Models with Normalizing FlowsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-06Full publication date if available
- Authors
-
Nu Hoang, Bao Duong, Thin NguyenList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.04980Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.04980Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.04980Direct OA link when available
- Concepts
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Nonlinear system, Econometrics, Causal model, Computer science, Statistical physics, Data science, Mathematics, Physics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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