Heteroscedastic Causal Structure Learning Article Swipe
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover the DAGs with polynomial time complexity under the equal variances assumption. However, this prohibits the heteroscedasticity of the noise, which allows for more flexible modeling capabilities, but at the same time is substantially more challenging to handle. In this study, we tackle the heteroscedastic causal structure learning problem under Gaussian noises. By exploiting the normality of the causal mechanisms, we can recover a valid causal ordering, which can uniquely identify the causal DAG using a series of conditional independence tests. The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality. In addition, via extensive empirical evaluations on a wide range of both controlled and real datasets, we show that the proposed HOST method is competitive with state-of-the-art approaches in both the causal order learning and structure learning problems.
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.3233/faia230321
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230321
- OA Status
- hybrid
- Cited By
- 2
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387172104
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387172104Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3233/faia230321Digital Object Identifier
- Title
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Heteroscedastic Causal Structure LearningWork title
- Type
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-28Full publication date if available
- Authors
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Bao Duong, Thin NguyenList of authors in order
- Landing page
-
https://doi.org/10.3233/faia230321Publisher landing page
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230321Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230321Direct OA link when available
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Heteroscedasticity, Directed acyclic graph, Causal structure, Conditional independence, Computer science, Artificial intelligence, Causal model, Machine learning, Independence (probability theory), Mathematics, Algorithm, Statistics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.modeling | 58 |
| abstract_inverted_index.possible | 31 |
| abstract_inverted_index.problem. | 20 |
| abstract_inverted_index.proposed | 159 |
| abstract_inverted_index.uniquely | 102 |
| abstract_inverted_index.STructure | 120 |
| abstract_inverted_index.addition, | 140 |
| abstract_inverted_index.algorithm | 129 |
| abstract_inverted_index.datasets, | 154 |
| abstract_inverted_index.effective | 125 |
| abstract_inverted_index.empirical | 143 |
| abstract_inverted_index.extensive | 142 |
| abstract_inverted_index.normality | 88 |
| abstract_inverted_index.ordering, | 99 |
| abstract_inverted_index.problems. | 176 |
| abstract_inverted_index.prohibits | 47 |
| abstract_inverted_index.structure | 79, 127, 174 |
| abstract_inverted_index.variances | 43 |
| abstract_inverted_index.approaches | 166 |
| abstract_inverted_index.complexity | 39 |
| abstract_inverted_index.controlled | 151 |
| abstract_inverted_index.exploiting | 86 |
| abstract_inverted_index.learning), | 121 |
| abstract_inverted_index.polynomial | 37 |
| abstract_inverted_index.Heretofore, | 0 |
| abstract_inverted_index.assumption. | 44 |
| abstract_inverted_index.challenging | 19, 68 |
| abstract_inverted_index.competitive | 163 |
| abstract_inverted_index.conditional | 111 |
| abstract_inverted_index.evaluations | 144 |
| abstract_inverted_index.mechanisms, | 92 |
| abstract_inverted_index.cause-effect | 10 |
| abstract_inverted_index.independence | 112 |
| abstract_inverted_index.polynomially | 132 |
| abstract_inverted_index.capabilities, | 59 |
| abstract_inverted_index.observational | 14 |
| abstract_inverted_index.relationships | 11 |
| abstract_inverted_index.substantially | 66 |
| abstract_inverted_index.computationally | 18 |
| abstract_inverted_index.dimensionality. | 138 |
| abstract_inverted_index.heteroscedastic | 77 |
| abstract_inverted_index.(Heteroscedastic | 118 |
| abstract_inverted_index.state-of-the-art | 165 |
| abstract_inverted_index.heteroscedasticity | 49 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.8016872 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |