SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation.
Liuyi Yao|Yaliang Li|Sheng Li|Mengdi Huai|Jing Gao|Aidong Zhang
| Anthology ID: | DBLP:conf/cikm/YaoLLHGZ21 |
|---|---|
| Volume: | CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 |
| Year: | 2021 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 3583-3587 |
| URL: | https://doi.org/10.1145/3459637.3482175 |
| DOI: | https://doi.org/10.1145/3459637.3482175 |
| DBLP: | conf/cikm/YaoLLHGZ21 |
| BibTeX: |
@inproceedings{yao-2021-sci,
author = {Liuyi Yao and
Yaliang Li and
Sheng Li and
Mengdi Huai and
Jing Gao and
Aidong Zhang},
editor = {Gianluca Demartini and
Guido Zuccon and
J. Shane Culpepper and
Zi Huang and
Hanghang Tong},
title = {{SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation}},
booktitle = {{CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021}},
pages = {3583--3587},
publisher = {ACM},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482175},
doi = {https://doi.org/10.1145/3459637.3482175}
}