abstract | booktitle | title | year | layout | series | publisher | issn | id | month | tex_title | firstpage | lastpage | page | order | cycles | bibtex_author | author | date | address | container-title | volume | genre | issued | extras | ||||||||||||||||
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The gold-standard approach to estimating heterogeneous treatment effects (HTEs) is randomized controlled trials (RCTs)/controlled experimental studies, where treatment randomization mitigates confounding biases. However, experimental data are usually small in sample size and limited in subjects’ diversity due to expensive costs. On the other hand, large observational studies (OSs) are becoming increasingly popular and accessible. However, OSs might be subject to hidden confounding whose existence is not testable. We develop an integrative |
First Conference on Causal Learning and Reasoning |
Integrative |
2022 |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
wu22a |
0 |
Integrative |
904 |
926 |
904-926 |
904 |
false |
Wu, Lili and Yang, Shu |
|
2022-06-28 |
Proceedings of the First Conference on Causal Learning and Reasoning |
177 |
inproceedings |
|