Normalizing flows for conditional independence testing Article Swipe
Bao Duong
,
Thin Nguyen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-023-01964-w
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-023-01964-w
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms, yet it remains a highly challenging problem due to dimensionality and complex relationships presented in data. In this study, we introduce LCIT (Latent representation-based Conditional Independence Test)—a novel method for conditional independence testing based on representation learning. Our main contribution involves a hypothesis testing framework in which to test for the independence between X and Y given Z , we first learn to infer the latent representations of target variables X and Y that contain no information about the conditioning variable Z . The latent variables are then investigated for any significant remaining dependencies, which can be performed using a conventional correlation test. Moreover, LCIT can also handle discrete and mixed-type data in general by converting discrete variables into the continuous domain via variational dequantization. The empirical evaluations show that LCIT outperforms several state-of-the-art baselines consistently under different evaluation metrics, and is able to adapt really well to both nonlinear, high-dimensional, and mixed data settings on a diverse collection of synthetic and real data sets.
Related Topics
Concepts
Conditional independence
Latent variable
Independence (probability theory)
Local independence
Representation (politics)
Computer science
Machine learning
Curse of dimensionality
Artificial intelligence
Latent variable model
Mathematics
Data mining
Theoretical computer science
Statistics
Law
Political science
Politics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10115-023-01964-w
- https://link.springer.com/content/pdf/10.1007/s10115-023-01964-w.pdf
- OA Status
- hybrid
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386216194
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386216194Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10115-023-01964-wDigital Object Identifier
- Title
-
Normalizing flows for conditional independence testingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-28Full publication date if available
- Authors
-
Bao Duong, Thin NguyenList of authors in order
- Landing page
-
https://doi.org/10.1007/s10115-023-01964-wPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10115-023-01964-w.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10115-023-01964-w.pdfDirect OA link when available
- Concepts
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Conditional independence, Latent variable, Independence (probability theory), Local independence, Representation (politics), Computer science, Machine learning, Curse of dimensionality, Artificial intelligence, Latent variable model, Mathematics, Data mining, Theoretical computer science, Statistics, Law, Political science, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.well | 166 |
| abstract_inverted_index.about | 97 |
| abstract_inverted_index.adapt | 164 |
| abstract_inverted_index.based | 53 |
| abstract_inverted_index.data. | 35 |
| abstract_inverted_index.first | 80 |
| abstract_inverted_index.given | 76 |
| abstract_inverted_index.infer | 83 |
| abstract_inverted_index.learn | 81 |
| abstract_inverted_index.mixed | 172 |
| abstract_inverted_index.novel | 47 |
| abstract_inverted_index.plays | 4 |
| abstract_inverted_index.sets. | 184 |
| abstract_inverted_index.test. | 122 |
| abstract_inverted_index.under | 156 |
| abstract_inverted_index.using | 118 |
| abstract_inverted_index.which | 66, 114 |
| abstract_inverted_index.causal | 17 |
| abstract_inverted_index.domain | 141 |
| abstract_inverted_index.handle | 127 |
| abstract_inverted_index.highly | 24 |
| abstract_inverted_index.latent | 85, 104 |
| abstract_inverted_index.method | 48 |
| abstract_inverted_index.really | 165 |
| abstract_inverted_index.study, | 38 |
| abstract_inverted_index.target | 88 |
| abstract_inverted_index.tasks, | 14 |
| abstract_inverted_index.(Latent | 42 |
| abstract_inverted_index.between | 72 |
| abstract_inverted_index.complex | 31 |
| abstract_inverted_index.contain | 94 |
| abstract_inverted_index.diverse | 177 |
| abstract_inverted_index.general | 133 |
| abstract_inverted_index.machine | 12 |
| abstract_inverted_index.problem | 26 |
| abstract_inverted_index.remains | 22 |
| abstract_inverted_index.several | 9, 152 |
| abstract_inverted_index.testing | 52, 63 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.discrete | 128, 136 |
| abstract_inverted_index.involves | 60 |
| abstract_inverted_index.learning | 13 |
| abstract_inverted_index.metrics, | 159 |
| abstract_inverted_index.settings | 174 |
| abstract_inverted_index.variable | 100 |
| abstract_inverted_index.Detecting | 1 |
| abstract_inverted_index.Moreover, | 123 |
| abstract_inverted_index.Test)—a | 46 |
| abstract_inverted_index.baselines | 154 |
| abstract_inverted_index.different | 157 |
| abstract_inverted_index.discovery | 18 |
| abstract_inverted_index.empirical | 146 |
| abstract_inverted_index.framework | 64 |
| abstract_inverted_index.introduce | 40 |
| abstract_inverted_index.learning. | 56 |
| abstract_inverted_index.performed | 117 |
| abstract_inverted_index.presented | 33 |
| abstract_inverted_index.remaining | 112 |
| abstract_inverted_index.synthetic | 180 |
| abstract_inverted_index.variables | 89, 105, 137 |
| abstract_inverted_index.collection | 178 |
| abstract_inverted_index.continuous | 140 |
| abstract_inverted_index.converting | 135 |
| abstract_inverted_index.especially | 15 |
| abstract_inverted_index.evaluation | 158 |
| abstract_inverted_index.hypothesis | 62 |
| abstract_inverted_index.mixed-type | 130 |
| abstract_inverted_index.nonlinear, | 169 |
| abstract_inverted_index.Conditional | 44 |
| abstract_inverted_index.algorithms, | 19 |
| abstract_inverted_index.challenging | 25 |
| abstract_inverted_index.conditional | 2, 50 |
| abstract_inverted_index.correlation | 121 |
| abstract_inverted_index.evaluations | 147 |
| abstract_inverted_index.information | 96 |
| abstract_inverted_index.outperforms | 151 |
| abstract_inverted_index.significant | 111 |
| abstract_inverted_index.statistical | 10 |
| abstract_inverted_index.variational | 143 |
| abstract_inverted_index.Independence | 45 |
| abstract_inverted_index.conditioning | 99 |
| abstract_inverted_index.consistently | 155 |
| abstract_inverted_index.contribution | 59 |
| abstract_inverted_index.conventional | 120 |
| abstract_inverted_index.independence | 51, 71 |
| abstract_inverted_index.investigated | 108 |
| abstract_inverted_index.dependencies, | 113 |
| abstract_inverted_index.relationships | 32 |
| abstract_inverted_index.dimensionality | 29 |
| abstract_inverted_index.independencies | 3 |
| abstract_inverted_index.representation | 55 |
| abstract_inverted_index.dequantization. | 144 |
| abstract_inverted_index.representations | 86 |
| abstract_inverted_index.state-of-the-art | 153 |
| abstract_inverted_index.high-dimensional, | 170 |
| abstract_inverted_index.representation-based | 43 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5102811209 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I149704539 |
| citation_normalized_percentile.value | 0.12185342 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |