Variational Causal Inference
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866915146487562240 |
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| author | Wu, Yulun Price, Layne C. Wang, Zichen Ioannidis, Vassilis N. Barton, Robert A. Karypis, George |
| author_facet | Wu, Yulun Price, Layne C. Wang, Zichen Ioannidis, Vassilis N. Barton, Robert A. Karypis, George |
| contents | Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited. In this case, to construct one's outcome under a counterfactual treatment, it is crucial to leverage individual information contained in its observed factual outcome on top of the covariates. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction under a counterfactual treatment: one source is the individual features embedded in the high-dimensional factual outcome; the other source is the response distribution of similar subjects (subjects with the same covariates) that factually received this treatment of interest. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2209_05935 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Variational Causal Inference Wu, Yulun Price, Layne C. Wang, Zichen Ioannidis, Vassilis N. Barton, Robert A. Karypis, George Machine Learning Artificial Intelligence Statistics Theory Genomics Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited. In this case, to construct one's outcome under a counterfactual treatment, it is crucial to leverage individual information contained in its observed factual outcome on top of the covariates. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction under a counterfactual treatment: one source is the individual features embedded in the high-dimensional factual outcome; the other source is the response distribution of similar subjects (subjects with the same covariates) that factually received this treatment of interest. |
| title | Variational Causal Inference |
| topic | Machine Learning Artificial Intelligence Statistics Theory Genomics |
| url | https://arxiv.org/abs/2209.05935 |