Causal Inference from Small High-dimensional Datasets Article Swipe
Raquel Aoki
,
Martin Ester
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.09281
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.09281
Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another high-dimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
Related Topics
Concepts
Causal inference
Inference
Estimator
Computer science
Observational study
Machine learning
Artificial intelligence
Feature (linguistics)
Sample size determination
Artificial neural network
Stability (learning theory)
Confounding
Lasso (programming language)
Outcome (game theory)
Data mining
Econometrics
Statistics
Mathematics
Philosophy
Linguistics
World Wide Web
Mathematical economics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2205.09281
- https://arxiv.org/pdf/2205.09281
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281260389
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4281260389Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2205.09281Digital Object Identifier
- Title
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Causal Inference from Small High-dimensional DatasetsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-19Full publication date if available
- Authors
-
Raquel Aoki, Martin EsterList of authors in order
- Landing page
-
https://arxiv.org/abs/2205.09281Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2205.09281Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2205.09281Direct OA link when available
- Concepts
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Causal inference, Inference, Estimator, Computer science, Observational study, Machine learning, Artificial intelligence, Feature (linguistics), Sample size determination, Artificial neural network, Stability (learning theory), Confounding, Lasso (programming language), Outcome (game theory), Data mining, Econometrics, Statistics, Mathematics, Philosophy, Linguistics, World Wide Web, Mathematical economicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.datasets. | 128 |
| abstract_inverted_index.estimates | 124 |
| abstract_inverted_index.stability | 114 |
| abstract_inverted_index.treatment | 7, 26, 74, 122 |
| abstract_inverted_index.complexity | 33 |
| abstract_inverted_index.especially | 52 |
| abstract_inverted_index.implicitly | 39 |
| abstract_inverted_index.inference. | 103 |
| abstract_inverted_index.techniques | 100 |
| abstract_inverted_index.confounding | 29 |
| abstract_inverted_index.estimators. | 56 |
| abstract_inverted_index.experiments | 105 |
| abstract_inverted_index.methodology | 71 |
| abstract_inverted_index.Causal-Batle, | 69 |
| abstract_inverted_index.application's | 20 |
| abstract_inverted_index.network-based | 55, 117 |
| abstract_inverted_index.observational | 10 |
| abstract_inverted_index.characteristics, | 21 |
| abstract_inverted_index.high-dimensional | 78, 85, 127 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |