Multi-treatment Effect Estimation from Biomedical Data Article Swipe
Raquel Aoki
,
Yizhou Chen
,
Martin Ester
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1142/9789811270611_0028
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1142/9789811270611_0028
Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural network that adopts a multi-task learning approach to estimate the effect of multiple treatments. We validated M3E2 in three synthetic benchmark datasets that mimic biomedical datasets. Our analysis showed that our method makes more accurate estimations than existing baselines.
Related Topics
Concepts
Causal inference
Benchmark (surveying)
Computer science
Machine learning
Inference
Task (project management)
Artificial intelligence
Focus (optics)
Estimation
Artificial neural network
Outcome (game theory)
Data mining
Econometrics
Mathematics
Optics
Physics
Geodesy
Mathematical economics
Management
Geography
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1142/9789811270611_0028
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4309847907Canonical identifier for this work in OpenAlex
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https://doi.org/10.1142/9789811270611_0028Digital Object Identifier
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Multi-treatment Effect Estimation from Biomedical DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-11-01Full publication date if available
- Authors
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Raquel Aoki, Yizhou Chen, Martin EsterList of authors in order
- Landing page
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https://doi.org/10.1142/9789811270611_0028Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1142/9789811270611_0028Direct OA link when available
- Concepts
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Causal inference, Benchmark (surveying), Computer science, Machine learning, Inference, Task (project management), Artificial intelligence, Focus (optics), Estimation, Artificial neural network, Outcome (game theory), Data mining, Econometrics, Mathematics, Optics, Physics, Geodesy, Mathematical economics, Management, Geography, EconomicsTop 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.adopts | 38 |
| abstract_inverted_index.causal | 13 |
| abstract_inverted_index.effect | 14, 46 |
| abstract_inverted_index.method | 67 |
| abstract_inverted_index.neural | 35 |
| abstract_inverted_index.showed | 64 |
| abstract_inverted_index.single | 27 |
| abstract_inverted_index.Several | 0 |
| abstract_inverted_index.contain | 3 |
| abstract_inverted_index.network | 36 |
| abstract_inverted_index.propose | 33 |
| abstract_inverted_index.accurate | 70 |
| abstract_inverted_index.analysis | 63 |
| abstract_inverted_index.approach | 42 |
| abstract_inverted_index.datasets | 57 |
| abstract_inverted_index.estimate | 11, 44 |
| abstract_inverted_index.existing | 20, 73 |
| abstract_inverted_index.however, | 24 |
| abstract_inverted_index.learning | 41 |
| abstract_inverted_index.methods, | 23 |
| abstract_inverted_index.multiple | 4, 48 |
| abstract_inverted_index.outcome. | 18 |
| abstract_inverted_index.Inference | 22 |
| abstract_inverted_index.benchmark | 56 |
| abstract_inverted_index.datasets. | 61 |
| abstract_inverted_index.synthetic | 55 |
| abstract_inverted_index.validated | 51 |
| abstract_inverted_index.baselines. | 74 |
| abstract_inverted_index.biomedical | 1, 60 |
| abstract_inverted_index.multi-task | 40 |
| abstract_inverted_index.treatments | 5 |
| abstract_inverted_index.estimations | 71 |
| abstract_inverted_index.treatments. | 28, 49 |
| abstract_inverted_index.applications | 2 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.1424848 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |