Deep Learning of Causal Structures in High Dimensions Article Swipe
Kai Lagemann
,
Christian Lagemann
,
Bernd Taschler
,
Sach Mukherjee
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.04866
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.04866
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach. Empirical results include linear and nonlinear simulations (where the underlying causal structures are known and can be directly compared against), as well as a real biological example where the models are applied to high-dimensional molecular data and their output compared against entirely unseen validation experiments. These results demonstrate the feasibility of using deep learning approaches to learn causal networks in large-scale problems spanning thousands of variables.
Related Topics
Concepts
Artificial intelligence
Computer science
Causality (physics)
Deep learning
Machine learning
Causal model
Causal inference
Causal structure
Scalability
Biomedicine
Intersection (aeronautics)
Convolutional neural network
Deep neural networks
Mathematics
Econometrics
Engineering
Biology
Physics
Statistics
Aerospace engineering
Database
Genetics
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.04866
- https://arxiv.org/pdf/2212.04866
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311251604
All OpenAlex metadata
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https://openalex.org/W4311251604Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.04866Digital Object Identifier
- Title
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Deep Learning of Causal Structures in High DimensionsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-09Full publication date if available
- Authors
-
Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach MukherjeeList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.04866Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.04866Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2212.04866Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Causality (physics), Deep learning, Machine learning, Causal model, Causal inference, Causal structure, Scalability, Biomedicine, Intersection (aeronautics), Convolutional neural network, Deep neural networks, Mathematics, Econometrics, Engineering, Biology, Physics, Statistics, Aerospace engineering, Database, Genetics, Quantum mechanicsTop 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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| abstract_inverted_index.flexible | 62 |
| abstract_inverted_index.learning | 32, 119 |
| abstract_inverted_index.networks | 53, 124 |
| abstract_inverted_index.problems | 127 |
| abstract_inverted_index.progress | 5 |
| abstract_inverted_index.scalable | 64 |
| abstract_inverted_index.spanning | 128 |
| abstract_inverted_index.Empirical | 66 |
| abstract_inverted_index.Motivated | 14 |
| abstract_inverted_index.against), | 85 |
| abstract_inverted_index.approach. | 65 |
| abstract_inverted_index.causality | 10 |
| abstract_inverted_index.empirical | 41 |
| abstract_inverted_index.framework | 58 |
| abstract_inverted_index.involving | 18 |
| abstract_inverted_index.learning. | 13 |
| abstract_inverted_index.molecular | 100 |
| abstract_inverted_index.nonlinear | 71 |
| abstract_inverted_index.thousands | 129 |
| abstract_inverted_index.variables | 36 |
| abstract_inverted_index.approaches | 120 |
| abstract_inverted_index.biological | 91 |
| abstract_inverted_index.knowledge. | 46 |
| abstract_inverted_index.particular | 22 |
| abstract_inverted_index.scientific | 16 |
| abstract_inverted_index.structures | 77 |
| abstract_inverted_index.underlying | 75 |
| abstract_inverted_index.validation | 109 |
| abstract_inverted_index.variables. | 131 |
| abstract_inverted_index.combination | 39 |
| abstract_inverted_index.demonstrate | 113 |
| abstract_inverted_index.feasibility | 115 |
| abstract_inverted_index.large-scale | 126 |
| abstract_inverted_index.simulations | 72 |
| abstract_inverted_index.applications | 17 |
| abstract_inverted_index.architecture | 30 |
| abstract_inverted_index.biomedicine, | 24 |
| abstract_inverted_index.experiments. | 110 |
| abstract_inverted_index.intersection | 8 |
| abstract_inverted_index.convolutional | 49 |
| abstract_inverted_index.relationships | 34 |
| abstract_inverted_index.high-dimensional | 19, 99 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |