CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation-Reference-Cited by-同舟云学术

CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation

Published:2024-08-24 Issue: Volume:33 Page:3013-3023
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Fan¹^ORCID,Chen Chaochao¹^ORCID,Liu Weiming¹^ORCID,Fan Tianhao²^ORCID,Liao Xinting¹^ORCID,Tan Yanchao³^ORCID,Qi Lianyong²^ORCID,Zheng Xiaolin¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

2. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China

3. College of Computer and Data Science/College of Software, Fuzhou University, Fuzhou, China

Funder

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672054

Reference63 articles.

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4. Peter C. Austin. 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research, Vol. 46, 3 (2011), 399--424.

5. Mathieu Blondel, Vivien Seguy, and Antoine Rolet. 2018. Smooth and sparse optimal transport. In International conference on artificial intelligence and statistics. PMLR, 880--889.