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Adversarial de-confounding in individualised treatment effects estimation

Abstract:

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversari...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-8195-548X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-4496-1896
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1552-5630
Publisher:
Proceedings of Machine Learning Research
Host title:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics
Series:
Proceedings of Machine Learning Research
Volume:
206
Pages:
837-849
Publication date:
2023-04-24
Acceptance date:
2023-01-20
Event title:
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
Event location:
Palau de Congressos, Valencia, Spain
Event website:
http://aistats.org/aistats2023/
Event start date:
2023-04-25
Event end date:
2023-04-27
ISSN:
2640-3498
Language:
English
Pubs id:
1287199
Local pid:
pubs:1287199
Deposit date:
2022-10-23

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