Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation-Reference-Cited by-同舟云学术

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
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Author:

Zhao Haoran¹,Zhang Yinghao¹,Cheng Debo²,Li Chen³,Feng Zaiwen⁴

Affiliation:

1. College of Science, Huazhong Agricultural University,Wuhan,China,430070

2. College of Computer Science and Engineering, Guangxi Normal University,Guilin,China,541000

3. Kyushu Institute of Technology,Department of Computer Science and Systems Engineering,Iizuka,Japan,820-8502

4. College of Informatics, Huazhong Agricultural University,Wuhan,China,430070

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10074610/10074618/10074882.pdf?arnumber=10074882

Reference43 articles.

1. Matching to Remove Bias in Observational Studies

2. Estimating individual treatment effect: generalization bounds and algorithms;shalit;Proceedings of International Conference on Machine Learning,0

3. The central role of the propensity score in observational studies for causal effects

4. Causal effect inference with deep latent-variable models;louizos;Proceedings of Conference on Neural Information Processing Systems,0

5. On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

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