Causal-StoNet: Causal Inference for High-Dimensional Complex Data Article Swipe
Yaxin Fang
,
Faming Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.18994
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.18994
With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear. As a result, the task of making causal inference with high-dimensional complex data has become a fundamental problem in many disciplines, such as medicine, econometrics, and social science. However, the existing methods for causal inference are frequently developed under the assumption that the data dimension is low or that the underlying data generation process is linear or approximately linear. To address these challenges, this paper proposes a novel causal inference approach for dealing with high-dimensional complex data. The proposed approach is based on deep learning techniques, including sparse deep learning theory and stochastic neural networks, that have been developed in recent literature. By using these techniques, the proposed approach can address both the high dimensionality and unknown data generation process in a coherent way. Furthermore, the proposed approach can also be used when missing values are present in the datasets. Extensive numerical studies indicate that the proposed approach outperforms existing ones.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.18994
- https://arxiv.org/pdf/2403.18994
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393335264
All OpenAlex metadata
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https://openalex.org/W4393335264Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.18994Digital Object Identifier
- Title
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Causal-StoNet: Causal Inference for High-Dimensional Complex DataWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-03-27Full publication date if available
- Authors
-
Yaxin Fang, Faming LiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.18994Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2403.18994Direct 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/2403.18994Direct OA link when available
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Causal inference, Inference, Computer science, Causal model, Econometrics, Data science, Artificial intelligence, Economics, Mathematics, StatisticsTop 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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