Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method.
Kairong Han|Weidong Huang|Taiyang Zhou|Peng Zhen|Kun Kuang
| Anthology ID: | DBLP:conf/cikm/Han0Z0K25 |
|---|---|
| Volume: | Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025 |
| Year: | 2025 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 5715-5722 |
| URL: | https://doi.org/10.1145/3746252.3761532 |
| DOI: | https://doi.org/10.1145/3746252.3761532 |
| DBLP: | conf/cikm/Han0Z0K25 |
| BibTeX: |
@inproceedings{han-2025-augmenting,
author = {Kairong Han and
Weidong Huang and
Taiyang Zhou and
Peng Zhen and
Kun Kuang},
editor = {Meeyoung Cha and
Carl Yang and
Senjuti Basu Roy and
Chanyoung Park and
Zhenhui Jessie Li and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Zhenhui Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {{Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method}},
booktitle = {{Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025}},
pages = {5715--5722},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3746252.3761532},
doi = {https://doi.org/10.1145/3746252.3761532}
}